S. Ji. 1

(This manuscript is based on the four lectures given at the Department of Romance Languages, Rovira i Virgili University in Tarragona, Spain, in 2003)

Semiotics of Life: A Unified Theory of Molecular Machines, Cells, the Mind, Peircean Signs, and the Universe based on the Principle of Information-Energy Complementarity

Sungchul JI Department of Pharmacology and Toxicology Rutgers University Piscataway, N.J. 08855, USA [email protected]

S. Ji. 2

TABLE OF CONTENTS ABSTRACT ...... 5

1. INTRODUCTION ...... 5

2. AN OVERVIEW ...... 6

2.1 AN INTEGRATION OF FRAGMENTED SCIENCES...... 6 2.2 SEMIOTICS: A PRELUDE ...... 7

PART I PRINCIPLES

3 AXIOMS, DEFINITIONS, AND LAWS ESSENTIAL FOR UNDERSTANDING THE PHENOMENON OF LIFE ...... 11

3.1 THE GNERGY PRINCIPLE ...... 11 3.2 THE PRINCIPLE OF GENERALIZED COMPLEMENTARITY ...... 12 3.3 COMPLEMENTARIAN LOGIC ...... 13 3.4 THE PRINCIPLE OF IRREDUCIBLE TRIADICITY ...... 14 3.5 THE PRINCIPLE OF MÖBIUS RELATIONS ...... 14 3.6 THE PRINCIPLE OF CLOSURE ...... 15 3.6.1. Semantic Closure...... 16 3.6.2 Principle of Ontological and Epistemic Closure...... 16 3.6.3 The Yin and Yang Principle...... 16 3.6.4 The Diachronic and Synchronic Closure...... 16 3.6.5 The Closure Relation between Boundary Conditions and the Dynamics of Physical Systems. ..16 3.6.6 The Anthropic Principle...... 16 3.7 ISOMORPHISM BETWEEN CELL AND HUMAN LANGUAGES ...... 17 3.8 THE ‘TABLE THEORY’ ...... 19 3.9 THE GENERALIZED FRANCK-CONDON PRINCIPLE ...... 21 3.10 THE PRAGMATIC MAXIM ...... 23 3.11 THE TAOISTIC MAXIM ...... 23 3.12 THE PRINCIPLE OF SELF-ORGANIZATION ...... 24 3.13 THE PRINCIPLE OF DYNAMIC BALANCE BETWEEN PRODUCTION AND DEGRADATION (PDBPD) ...... 25 3.14 THE PRINCIPLE OF INTRACELLULAR DISSIPATIVE STRUCTURES (PIDS) ...... 25 3.15 THE PRINCIPLE OF BIOACTIVITY COEFFICIENTS ...... 25 3.16 THE PRINCIPLE OF RULE-GOVERNED CREATIVITY ...... 26 3.17 THE PRINCIPLE OF RECURSIVITY ...... 26

PART II MOLECULES

4 WHAT IS LIFE? ...... 27

4.1 DEFINITION OF LIFE ...... 27 4.2 LIFE ACCORDING TO SCHRÖDINGER ...... 27 4.3 LIFE ACCORDING TO PRIGOGINE ...... 28 4.4 LIFE ACCORDING TO PATTEE ...... 31 4.5 LIFE BASED ON THE PRINCIPLE OF INFORMATION-ENERGY COMPLEMENTARITY...... 31 5 THE CONFORMON THEORY OF MOLECULAR MACHINES AND MOTORS ...... 32

5.1 CONFORMONS: EXPERIMENTAL EVIDENCE ...... 32 5.2 MOLECULAR MECHANISMS OF CONFORMON GENERATION BASED ON THE GENERALIZED FRANCK- CONDON PRINCIPLE ...... 38 S. Ji. 3

5.3 THE CONFORMON HYPOTHESIS OF ENERGY-COUPLED PROCESSES IN THE CELL ...... 38 5.4 DECONSTRUCTING THE CHEMIOSMOTIC HYPOTHESIS ...... 39 5.5 THE INFORMATION-ENERGY LANDSCAPE THEORY OF PROTEIN FOLDING ...... 43 5.6 DECODING DNA BASED ON THE SEMIOTIC LESSONS LEARNED FROM DECODING THE ROSETTA STONE ...... 45

PART III CELLS

6 THE CELL AS THE ATOM OF SEMIOSIS ...... 51

6.1 THE BHOPALATOR ...... 51 6.2 IDSS (INTRACELLULAR DISSIPATIVE STRUCTURES) ...... 53 6.3 COMPLEMENTARY DNA ARRAYS AND NEW CELL BIOLOGY ...... 54 6.4 AN ANALOGY BETWEEN ATOMIC PHYSICS AND CELL BIOLOGY ...... 59 6.5 RIBONOMICS ...... 62 6.6 DSSIPATIVE STRUCTURES AS THE THIRD ARTICULATION IN CELL BIOLOGY ...... 63 6.7 THREE CLASSES OF DISSIPATIVE STRUCTURES – WITH FIXED, MOVING, AND INFORMED BOUNDARIES ...... 66 6.8 THE TRIADIC STRUCTURES OF THE LIVING CELL ...... 68 6.9 A TOPOLOGICAL MODEL OF THE LIVING CELL ...... 69

PART IV THE MIND

7. SEMIOTICS AND LIFE SCIENCES ...... 73

7.1 THE BIOLOGY-LINGUISTICS CONNECTION ...... 73 7.2 SEMIOTICS: THE PEIRCEAN THEORY OF SIGNS ...... 76 7.3 PEIRCEAN DEFINITION OF SIGNS ...... 76 7.4 PEIRCEAN SIGNS AS GNERGONS ...... 78 7.5 THE QUARK MODEL OF PEIRCEAN SIGNS ...... 80 7.5.1 9 Types of Signs ...... 81 7.5.2 Ten Classes of Signs ...... 83 7.5.3 The Derivation of the 10 Classes of Signs from 9 Types of Signs Based on the Analogy between e-Signs and Quarks in Elementary Particle Physics...... 84 7.5.4 An Application of the Concept of c-Signs to Molecular Biology: Microsemiotics ...... 85 7.6 SEMIOSIS: REAL VS. VIRTUAL ...... 86 7.7 THE ORIGINS OF BIOLOGICAL INFORMATION AND LIFE ...... 89 7.8 THE VON NEUMANN QUESTIONS AND THE CONFORMON THEORY ...... 96 8 PEIRCE’S METAPHYSICS AS THE BASIS FOR UNIFYING SCIENCES ...... 98

8.1 MACRO- VS. MICROSEMIOTICS ...... 100 8.2 HUMAN AND CELL LANGUAGES AS MANIFESTATIONS OF ‘COSMOLANGUAGE’...... 101 8.3 COMPLEMENTARISM AND SEMIOTICS ...... 103 8.4 SIGNS, THOUGHTS, AND ‘THOUGHTONS’ ...... 105 8.5 A TOPOLOGICAL THEORY OF THE MIND ...... 108 8.6 THE COGNITIVE UNCERTAINTY PRINCIPLE ...... 110 9 THE INFORMATION-ENERGY RELATION ...... 111

9.1 A ‘PHILOSOPHICAL TABLE’ FOR CLASSIFYING INFORMATIONS, ENTROPIES, AND ENERGIES ...... 112 9.2 THE INFORMATION-ENERGY-ENTROPY RELATION: THE ‘NEWJERSEYATOR’ ...... 115 9.3 SEMIOTICS AND INFORMATION THEORY ...... 117

S. Ji. 4

PART V THE UNIVERSE

10 A MODEL OF THE UNIVERSE BASED ON THE GNERGY TETRAHEDRON ...... 119

10.1 THE SHILLONGATOR MODEL OF THE UNIVERSE ...... 119 10.2 A THEORY OF THE ORIGIN OF INFORMATION BASED ON PEIRCEAN METAPHYSICS ...... 122 10.3 THE SELF-KNOWING UNIVERSE AND THE ANTHROPIC COSMOLOGICAL PRINCIPLE...... 124 10.4 SEMIOTICS AS THE THEORY OF EVERYTHING (TOE) ...... 128 10.5 ICONIC MODEL OF REALITY ...... 134 10.6 LIFE AS AN INTRINSIC ASPECT OF THE UNIVERSE ...... 137 10.7 SEMIOTICS OF THE UNIVERSE ...... 140 11 CONCLUSIONS ...... 145

ACKNOWLEDGEMENT...... 146

REFERENCES: ...... 146

APPENDICES ...... 157

APPENDIX I COMPLEMENTARITY VS. SUPPLEMENTARITY...... 157 APPENDIX II THE DEFINITION OF CONFORMATIONS ...... 160 APPENDIX III. THE ‘APOPTOSIS-CHEMIOSMOSIS’ PARADOX ...... 163 APPENDICX IV. DECODING THE DNA TEXT ...... 166 APPENDIX V. AN INTELLECTUAL CRISIS IN THE FIELD OF DNA MICROARRAY DATA ANALYSIS ...... 170 APPENDIX VI. LAWS OF MIROARRAY DATA INTERPRETATION (I)...... 172 APPENDIX VII. THE LAWS OF MICROARRAY DATA INTERPRETATION (II) ...... 174 APPENDIX VIII. THE ALGEBRA OF COMPLEMENTARISM ...... 176 APPENDIX IX. PEIRCEAN SIGNS AS GNERGONS ...... 179 APPENDIX X. ‘COSMOLANGUAGE’ (I) ...... 180 APPENDIX XI. ‘COSMOLANGUAGE’ (II) ...... 185 APPENDIX XII. TAXONOMY OF ENTROPY-INFORMATION RELATIONS ...... 188

S. Ji. 5

Abstract

A theory of everything (TOE) is described that accounts for a wide range of phenomena, including the structure and function of the Universe, molecular machines, living cells, the human body, the mind, and Peircean signs. This theory is constructed by combining two major components – i) the earlier TOE known as the Shillongator reported in 1991 that embodies the principle of energy-information complementarity, and ii) the theory of signs formulated by the American chemist-logician-philosopher, C. S. Peirce (1839-1914), in the late 19th and early 20th century. The new TOE (to be called the Tarragonator to indicate its origin in the lectures that the author delivered in Tarragona in 2003) is a Peircean sign (or a cosmic code) that can be characterized as having the following irreducible triad—i) the body-centered tetrahedron (see Figure 59) serving as the Representamen (also called sign vehicle), ii) our Universe as its Object, and iii) the newly postulated language called cosmolanguage as the Interpretant (i.e., the third entity by which the interpreter is able to connect Representamen to Object). The existence of ‘cosmolanguage’ was inferred from the isomorphism between cell and human languages found in 1997, but its possible connection to the structure and function of our Universe was unknown until now. Because the definition of signs given by Peirce plays a crucial role in formulating the Tarragonator as a TOE, the basic definitions and principles underlying Peircean semiotics, the science of signs, are reviewed in some detail. Also some of the key results from my earlier work in enzymology (including molecular machines and motors), theoretical cell biology, and cell language theory are reviewed and summarized, since they form the empirical foundation on which the Tarragonator stands. One of the interesting consequences of the Tarragonator is the novel notion that life can be viewed as highly condensed form of information, just as physicists consider matter as highly condensed form of energy.

1. Introduction

In the summer of 2003, I gave four lectures at the Rovira i Virgili University in Tarragona, Spain, at the invitation of Professor Carlos Martin-Vide, the director of the Mathematical Linguistics Program and the chair of the Department of Romance Languages at the University. The present contribution originated from one of these lectures, entitled Semiotics of Life, a title that was assigned to me by Carlos, most likely because I had published several papers discussing a possible molecular theoretical link between natural language and the cell language, the language used by living cells [1-5]. Semiotics of Life is the most difficult manuscript that I have ever written in the past three decades. This in part explains why it took me so long (over 2 years!) to finish the manuscript, not because I had neglected it but because I, as a chemist-turned theoretical cell biologist, needed the time to broaden as well as deepen my knowledge on many subjects deemed essential to understand the phenomenon of life, including computer science, information theory, logic, quantum physics, philosophy, semiotics itself, and, most recently, the exciting new developments in cell biology set in motion by the revolutionary experimental technique known as cDNA arrays (see Section 6.3) invented S. Ji. 6 in the mid-1990’s [6-9]. Here I present the result of my theoretical investigations carried out between August, 2003 and July, 2005. My theoretical work was greatly stimulated and aided by my almost daily discussions with some of the members of the NECSI (New England Complex Systems Institute, Boston, Mass) mailing list [10]. Therefore, this article may be viewed as a summary, as of July, 2005, of my bio-theoretical investigations that began in 1970, when I was a postdoctoral fellow in the laboratory of the late David E. Green at the Institute of Enzyme Research at the University of Wisconsin in Madison. As will become evident below, the unifying theme that has captivated me and occupied my attention continuously since the early 1970’s is the concept of the complementarity between information and energy. I was led to invoke this concept to account for the molecular mechanisms underlying the phenomenon of the so- called oxidative phosphorylation (also called oxphos) that occur in mitochondria, the power house of the cell. Oxphos refers to the synthesis of ATP, the major energy currency in the living cell, by phosphorylating ADP driven by the free energy released from the oxidation of substrates. When D. E. Green and I invoked this concept in 1972 [74], I never dreamed that, three decades later, the same concept would be found useful in explaining the structure and function of many other systems, including the Universe itself, molecular machines, the living cell, the human body, the mind, and the sign defined by Peirce. In the following pages, the empirical and theoretical evidences are discussed that support the theory of everything (TOE) here called the Tarragonator that was motivated by (and contains as its most important building block) the principle of information-energy complementarity. Information is defined in this article as the ability of a physical system to select or control, given energy, and energy will be defined as the ability of a physical system to do work, either controlled or uncontrolled.

2. An Overview

2.1 An Integration of Fragmented Sciences

The topics covered in this contribution are unusually broad and diverse, ranging from ancient philosophies and semiotics to most recent discoveries in molecular and cell biology, including DNA microarray experiments. To me all these traditionally separate fields of human knowledge are but different instantiations of one central concept:

The ultimate reality appears to human mind either as information (1) or as energy, depending on how it is viewed, just as light behaves as waves or particles depending on how it is measured.

In the following discussions, the expression ‘energy’ will be used interchangeably with ‘energy/matter’, since energy, E, and matter, M, are interconvertible through the Einstein’s equation, E = mc2, where c is the speed of light and m is the rest mass of an object. According to the view expressed in Statement (1), which is the essence of the new biology-based philosophical framework known as complementarism [11], all sciences (i.e., organized systems of human knowledge rooted in experience) can be divided into S. Ji. 7 five major groups, depending on how information (I) and energy (E) are thought to be related to each other (see Table 1).

Table 1. The division of sciences into 5 branches based on the information-energy relations. I = information; E = energy/matter.

Science Example 1. Information Primacy Plato’s philosophy (?) (I is primary and E derives from it.) Most modern philosophies Peirce’s semiotics [12-17] (?) Logic Mathematics Modern molecular biology Wheeler’s “It from Bit” [18] 2. Energy Primacy Most modern physical sciences (?) (E is primary and I derives from it.) Bohr’s complementarity (?) [19-22] Economics (?) 3. Information and Energy Identity Brillouin [23] (I and E are identical.) Collier [24] 4. Information and Energy Supplementarity Bennett [25] (I and E are intrerconvertible.) Layzer [26]

5. Information/Energy Complementarity Aristotle’s hylomorphism [27] (I and E are complementary) Spinoza’s Substance [28, 188] Merleau-Ponty’s Flesh [29] Complementarism [11, 30] The conformon theory of molecular machines [31-34] The cell language theory [1-5] Molecular information theory [35]

As evident in Table 1, the information and energy relation plays a fundamental role in all branches of human knowledge, and this article is based on one of these possible relations between I and E, namely, the information/energy complementarity.

2.2 Semiotics: A Prelude

Semiotics is the scientific study of signs [12-17]. According to the American chemist-logician-philosopher Charles S. Peirce (1839-1914) [36],

"A sign, . . . , is something which stands to somebody for (1) something in some respect or capacity." [12, p. 99]. S. Ji. 8

Thus, ‘apple’ is a sign referring to a juicy spherical fruit to someone, E, who speaks English. But ‘apple’ is not a sign for a Korean, K, who does not speak English. For K, the sign, S, for the same object, O, is not ‘apple’ but ‘sah-gwah’. From this simple example, it is evident that the definition of a sign, S, must include, in addition to O, a third element that Peirce referred to as interpretant, I, which is well explained in the following paragraph quoted in [189]:

“A sign is a thing which serves to convey knowledge of (1a) some other thing, which it is said to stand for or represent. This thing is called the object of the sign; the idea in the mind that the sign excites, which is a mental sign of the same object, is called an interpretant of the sign.”

Thus, we can understand the interpretant as the effect that S has on its interpreter or as the mechanisms or processes by which the interpreter or the processor of S is made to connect O and S. That is, in order for a sign process to occur successfully, there must be interactions among three elements, S, O, and I, within the sign processor. It was Peirce who first recognized the necessity of invoking these three elements in the definition of signs and their actions (which he called ‘semiosis’). In other words, a sign, according to Peirce, is an irreducible triad of S, O, and I, which idea is often referred to as the “irreducibility of the sign triad” or the “triadicity of signs.” It is important to note that, in this definition of a sign, the term ‘sign’ has dual meanings – as a component of the sign triad and as the sign triad itself. To distinguish between these two meanings, Peirce coined the term ‘representamen’ to refer to the narrower sense of the term sign [12, p. 121]. Thus, we may represent the Peircean definition of a sign graphically as follows: _ _ | | | R | | | S = | | | | | O I | |_ _|

Figure 1. A graphical representation of the Peircean sign triad. S = sign, R = representamen (also called sign), O = object, and I = interpretant. Unless pointed out otherwise, sign usually means R, a component of the irreducible sign triad. Also, it is important to note that the interpreter of R or the material system that processes R, thereby implementing semiosis, is not explicitly discussed in semiotics literature but is assumed to be present. We will use the triangle itself to represent this interpreter, thus graphically distinguishing between interpretant (one of the three apexes) and interpreter (the triangle). Please note that the bracket symbolizes the inrreducibility of Peircean sign triad: i.e., none of the three elements can be replaced by any other. S. Ji. 9

The definition of signs that Peirce formulated can be extended to molecular biology, although Peirce himself did not know of this possibility because he died about four decades before the field of molecular biology emerged as a branch of biological science. Genes encoded in DNA fit the definition of the Peircean sign, because they code and stand for their complementary transcripts, RNA molecules and their functions, which are evidently distinct from DNA. One plausible candidate for the interpretant for DNA viewed as a molecular sign is the state of the cell, since whether a given gene encoded in DNA is expressed or not depends on the state of the cell, leading to the following graphical representation of DNA as a molecular sign [5]:

DNA

DNA Sign =

RNA Cell State

Figure 2. Genes encoded in DNA as examples of Peircean signs at the molecular level. The role of interpretant is suggested to be fulfilled by cell states, and the interpreter of DNA is postulated to be the cell itself represented by the triangle. This definition seems consistent with the finding that only a select set of genes are expressed in a given cell at any given time and under a given environmental conditions, depending on the internal state of the cell.

Peirce distinguished between semiotics and semiosis. Semiotics is the systematic knowledge that human mind has created about semiosis based on empirical data, while semiosis refers to the totality of the natural and artificial processes whose occurrence requires the mediating role of signs (see Sections 7.3 and 7.4 for the classification of signs). Thus, we may logically conclude that, although semiotics depends on human mind, semiosis does not. The causal relation between semiotics and semiosis may be represented diagrammatically as shown in Figure 3.

Ontological Process ______| | | \/ Semiosis --> Cells --> Mind --> Language --> Semiotics /|\ | |______|

Epistemological Process

Figure 3. The cyclical, or reversible, relation between semiosis and semiotics. The expression ‘A -> B’ should be read as “B presupposes A” or “B cannot exist without A”. The upper arrow from left to right indicates the ontological process in the Universe known as evolution, while the lower arrow from right S. Ji. 10

to left signifies the epistemological causal relation resulting from human inferential activities of the mind. It is assumed that ontological processes are independent of human mind but epistemological processes are dependent on ontological processes. This figure is consistent with the principle of closure discussed in Section 3.6 below.

One corollary of Figure 3 is that the elucidation of the connection between semiotics and life, the main topic of this contribution, would be tantamount to elucidating the principles underlying semiosis itself, since life (as exemplified by cells and mind) presupposes semiosis. One of the most significant conclusions reached in this contribution is that, like all fundamental processes in nature, semiosis has two complementary aspects – the energetic/material (e.g., computer hardware, or ATP in cells) and the informational (e.g., computer software or genetic information encoded in DNA). Of these two aspects, the traditional semiotics as formulated by Peirce and Poinsot (1589-1644) [12-17, 37] has emphasized primarily the informational aspect of semiosis, apparently ignoring (or not being able to do anything about) the equally fundamental energetic/material aspect. It was only with the advances made in both experimental and theoretical branches of molecular and cell biology during the past several decades that the essentiality of the energy/material aspect of semiosis has emerged and recognized [31-35, 38]. Thus it has been postulated that all self-organizing processes in the Universe, including semiosis, are driven by a complementary union of information and energy, referred to as gnergy [11, 38]. Since information can be alternatively called ‘gnon’ (from the Greek root gnosis meaning knowledge) and energy ‘ergon’ (from Greek root ergon meaning work or energy), gnergy is a complementary union of the gnon and the ergon:

Gnergy = Gnon ^ Ergon (2) where the symbol “^” is used to denote a generalized complementrity relation as defined in [11] (see also Sections 3.2 and 3.3 below): That is, “A = B^C” should be read as “B and C are complementary aspects of A”, or “A is a complementary union of B and C”. It has been postulated that Gnergy defined in (2) serves as the universal driving force for all self-organizing processes in this Universe, including molecular processes in the living cell [38, p. 152-163]. Therefore, based on Figure 3 and Scheme (2), we can make the following general statement:

“Life results from semiosis driven by gnergy.” (3)

The remainder of this contribution discusses the multifarious aspects of Statement (3) divided into 5 parts under the rubrics of Principles, Molecules, Cells, The Mind, and The Universe. In the process, it will emerge that Statement (3) is embedded within a larger theoretical framework called the Tarragonator which can be best described in terms of the concept of a Peircean sign (to be defined in Sections 2.2 and 7.3): i) the body-centered tetrahedron as its Representamen, ii) the Universe (including everything in it from molecules to cells to mind) as its Object, and iii) a set of molecular theories S. Ji. 11

(including the conformon theory, the cell language theory, and molecular information theory) as its Interpretant.

Part I Principles

3 Axioms, Definitions, and Laws Essential for Understanding the Phenomenon of Life

The American paleontologist Gaylord Simpson [39] stated something to the effect that “Physicists study the principles that apply to all phenomena; (4) biologists study phenomena to which all principles apply.” In view of the potential importance of this statement, we may refer to it as the “Simpson conjecture”, the “Simpson thesis”, or the “Simpson doctrine”. Consistent with this thesis, I have found that, in constructing a coherent theory of life variously called biocybernetics [38], microsemiotics [1, 4, 5], or molecular information theory [35], it has been necessary to incorporate more than 30 principles, laws, and concepts, imported from physics, chemistry, engineering, computer science, mathematics, linguistics, and philosophy, of which 26 are listed in Table 5 in [1]. So I have devoted, on average, about one year to study each of these 30 conceptual items and apply it to the construction of a theory of biology during the past 30 years. To assist the reader to navigate through the theoretical forest without getting lost among the trees, it was thought prudent to provide a list of the key theoretical landmarks that will be encountered repeatedly throughout the remainder of this contribution.

3.1 The Gnergy Principle

As already indicated, all self-organizing processes in the Universe, both living and nonliving, are postulated to be driven by a physico-metaphysical entity called gnergy, a complementary union of information and energy [11, 30], metaphysical in the sense that the concept of gnergy cannot be proven by traditional quantitative methods of natural sciences. Information (e.g., software, the mechanical structure of a car) and energy (e.g., electricity, gasoline) can be separated in macroscopic machines, but in molecular machines in action (e.g., molecular motors, ATP-driven proton pumps, etc.), these two entities appear to exist as a fused entity called the gnergon (formed from three Greek roots, gn- meaning information, -erg- meaning work or energy, and –on meaning mobile entity or particle). Gnergons can be viewed as discrete units of gnergy. One concrete example of gnergons in action in molecular and cell biology is the conformon, the mechanical energy associated with conformational strains localized in sequence-specific sites within biopolymers (the experimental evidence for conformons is discussed in Section 5.1). S. Ji. 12

The gnergy principle is also referred to as the principle of information and energy complementarity (PIEC). According to this principle, all self-organizing processes in the Universe (e.g., the origin of life, physicochemical processes occurring in the living cell such as self-replication and chemotaxis, cognitive processes in the human brain, biological evolution, the evolution of the Universe, etc.) are driven by entities that comprise two complementary aspects, namely, information (carried by material entities or signs that have been selected from two or more similar entities because of their unique activities or behaviors) and energy (the ability of those entities to do work or cause changes of some kind). According to PIEC, therefore, the ATP molecule which plays a fundamental role in all self-organizing processes inside the cell must carry not only energy as is usually assumed (about 16 Kcal/mole under physiological conditions) but also information (encoded in its 3-dimensional molecular shapes, also called conformations: see Appendix II for the definition of conformations). Thus, it can be predicted that, for some biochemical processes driven by ATP, ATP cannot be replaced by deoxy-ATP even though the latter can also be hydrolyzed by ATPase to generate the same amount of free energy, because the deoxy-ATP molecule does not have the same information (i.e., shape) as ATP. An analogy may be helpful here: Although a US dollar bill and a Korean 1000 Won bill have approximately the same monetary value (energy), the latter cannot replace the former in a vending machine in the US because it has different information (i.e., a different shape, color, and texture) from that of a dollar bill. But it should be noted that, under some conditions, only one of the two complementary aspects may suffice for actions, in which case both ATP and deoxy-ATP should lead to same results, as often found to be the case in biochemical experiments . PIEC is expected to be manifested in the Universe in many different guises. The wave/particle complementarity is perhaps the best known example in science, and the principle of matter-symbol complementarity (PMSC), advanced by H. Pattee [40-43], may be viewed as another important manifestation of PIEC. According to PMSC (later renamed as the von Neumann-Pattee principle of matter-sign complementarity [3]), all self-reproducing systems have two complementary aspects – physical law-governed material/energetic aspect and the evolutionary rule-governed informational (or symbolic) aspect. Pattee asserts that open-ended evolution is possible if and only if evolving systems have both these aspects so as to effectuate what he calls a semantic closure[44, 45].

3.2 The Principle of Generalized Complementarity

The ultimate reality, C, is the complementary union of an irreconcilably opposite and dichotomous pair, A and B, which can be conveniently represented as follows:

C

A B S. Ji. 13

Figure 4.A diagrammatic representation of the principle of generalized complementarity: The ultimate reality, C, is a complementary union of irreconcilably opposite A and B. For example, C = light, if A = particle, and B = wave.

3.3 Complementarian Logic

The complementarian logic, consisting of just three elements to be described below, was formulated in the mid-1990's by generalizing Bohr's complementarity concept (see Appendix I) [190] in such a way that it could be applied to fields beyond physics, including biology, psychology, brain physiology, philosophy, and religion [11, 30]. In 1958, N. Bohr defined the concept of complementarity and the related concept of supplementarity (see Appendix I), and both of these concepts play crucial roles in the theory of life developed in this contribution. Recently, a similar attempt was made by a physicist-turned-cognitive scientist H. Atmanspacher who generalized algebraic quantum theory in the form of what is known as the 'weak quantum theory (WQT)' [46]. The logic behind WQT is expressed in a traditional mathematical language (i.e., using primarily symbolic signs), whereas the logic of complementarism has been expressed in terms of multimodal or heterogeneous reasoning [47] that utilizes not only the written language (symbolic signs) but also diagrams and tables (iconic signs). The complementarian logic has the following structure: 1) Exclusivity. A and B are mutually exclusive in the sense that A and B cannot be measured/observed/thought about simultaneously within a given context. Light can exhibit wave or particle properties depending on measuring instruments employed but no one has yet been able to measure these properties simultaneously under a given measuring environment (to the best of my knowledge). But, it is interesting to note that, on the theoretical level, particle and wave properties are not exclusive in the sense that they are related to each other through the de Broglie equation,  = h/mv, where h is the Planck constant, and  is the wavelength of the wave associated with a particle of mass m moving with a velocity v. Similarly, the Yin and the Yang are irreconcilably opposite in the Visible World but are merged into one in the Tao in the Invisible World (see Figure 1 in [11]). 2) Essentiality. A and B are both essential for completely describing/understanding a third term C. (Light cannot be described completely in terms of either particle or wave properties alone but both these properties are essential to our understanding of the nature of light, or of any other 'quantum objects' often called 'quons' [48].) 3) Transcendentality. C transcends the level of description where A and B have meanings and serve as the source of, or as the ground for, the irreconcilably opposite A and B. (The quality of light as directly perceived through the human eye transcends the level of instrument-mediated observations/measurements where it appears as either waves or particles.) These elements of the complementarian (or triune) logic can be conveniently represented as a triangle:

S. Ji. 14

C Level 2 ____ | | Transcendentality Level 1 ____| A B Figure 5. An iconic representation of the complementarian logic. Each node is occupied by one of the three logical elements, and the edges have the following meanings: A---B = Exclusivity; A---C or B---C = Essentiality; Levels 1 and 2 = Transcendentality.

3.4 The principle of irreducible triadicity

According to the metaphysics of Peirce, all phenomena, material or mental, living or nonliving, comprise three basic elements – Firstness (e.g., quality, feeling, possibilities), Secondness (e.g., facts, actualities, reaction, interaction, brute force), and Thirdness (e.g., generality, laws, habit-taking, representation, reasoning). For example, in logic, there are three and only three kinds of relations; C = monadic, A = dyadic, and B = triadic relation. We may represent this principle diagrammatically as follows: Firstness

Secondness Thirdness Figure 6. A diagrammatic representation of the principle of irreducible triadicity of Firstness, Secondness and Thirdness of Peirce [12-17]. The relation between the triangles shown here and in Figure 4 is not direct (see Section 8.3 for a related discussion).

3.5 The principle of Möbius relations

The Möbius strip (or band) is "a one-sided surface that is constructed from rectangle by holding one end fixed, rotating the opposite end through 180 degrees, and applying it to the first end" [Webster's Ninth New College Dictionary]. The essential geometric properties of the Möbius strip may be characterized in terms of the following two propositions: "The Möbius strip consists of two opposite surfaces, A and B, when viewed locally.” (5) "Surfaces, A and B, merge into one another when viewed globally." (6) Statements (5) and (6) may be combined into one: "Locally A or B; globally A and B." (7) A more general version of (7) would be: S. Ji. 15

”Locally A, B, C, D or E; globally A, B, C, D and E” (8) where any one or more of B, C, D, and E can be empty, thus leading to a set of 4 possible Möbius relations as summarized in Table 2. The collection of these relations may be referred to as the Möbius set.

Table 2. The characteristics of the Möbius relations

Möbius Relations Geometric Example Name1

Möbius dyad (A &B) Möbius strip Leipzigator2 Möbius triad (A, B & C) Peircean triad Milfordator3 [36]

Möbius tetrad (A, B, C & D) Spinks pyramid SanAntonio-ator4

Möbius pentad (A, B, C, D& E) Body-centered tetrahedron Tarragonator5 [51]

1The unusual-looking terms in this column derive from the tradition set in the field of physical chemistry where any self-organizing chemical reaction-diffusion systems are named as the “X-ator”, where X is the name of the city where the relevant research was carried out or is somehow related to the research, and “-ator” indicates a dynamic dissipative system [49, 50]. However, the ators named here differ from the usual ators discussed in physical chemistry and biology (see pp. 26-28 in [38]) in that they represent primarily geometric structures without any physical content, except when they are explicitly utilized to represent self-organizing physicochemical systems, as exemplified by the Tarragonator (see Section 10.7). 2From Leipzig where Dr. Paul Julius Möbius was born and worked as a Ph.D and MD in endocrinology and neurophysiology. 3From Milford, PA, where Peirce worked on his semiotics during the last three decades of his life [36]. This term was first used in [51]. 4From San Antonio where Dr. S. Spinks extended Peirce’s sign triad to a tetrad (see Section 8.4) [52-54]. 5From Tarragona, Spain, where I delivered a series of lectures, from which the notion arose of adopting the body-centered tetrahedron enclosed within a sphere (see Figure 59) as a geometric representation of a ‘super-category’, a generalization of the mathematical concept of category [51.

3.6 The principle of Closure

When two dichotomous entities, A and B, need each other for their own existence so that, without A, B cannot exist or function, or vice versa, A and B may be said to exhibit or embody the principle of closure.

S. Ji. 16

3.6.1. Semantic Closure. The principle of closure defined above was inspired by, and is a generalization of, the concept of “semantic closure” or “semiotic closure” formulated by H. Pattee [42, 44] who characterized semantic closure as follows: “ . . . this complex interrelationship of strong and weak bonds . . . that allows the realization of von Neumann’s quiescent symbolic description and dynamic material construction.”

3.6.2 Principle of Ontological and Epistemic Closure. Before the cell can read DNA, an epistemic act, the cell must have been endowed with such a capability through evolution, an ontological process. Before the human brain can know anything, an epistemic act, it must have been endowed with the knowing capability through biological evolution, an ontological act.

3.6.3 The Yin and Yang Principle. The Yin needs the Yang, and vice versa, to complete the circle of the Tao.

3.6.4 The Diachronic and Synchronic Closure. Although humans can use a language without knowing its past history (which is related to the synchronicity emphasized by Saussure [55]), a language cannot be effective as a means of communication among members of a linguistic community without its long history of development and evolution (diachronicity). Biologists can describe all the physics, chemistry, and biochemistry of the living cell (synchronicity), but it would be impossible for them to understand the workings of the cell without taking into account the history of biological evolution (diachronicity). This is because the synchronic properties of the cell (e.g., cell cycle, chemotaxis, etc.) are needed for its evolution and the evolutionary process is needed for the emergence of such properties.

3.6.5 The Closure Relation between Boundary Conditions and the Dynamics of Physical Systems. It is clear that no physical laws can describe any observable properties without there being specific initial and boundary conditions. In other words, the equation of motion describing a physical system which embodies laws of physics cannot be solved without the initial and boundary conditions applicable to that system [44].

3.6.6 The Anthropic Principle. Cosmologists have found that the numerical values of the fundamental physical constants such as c (speed of light), G (gravitational constant), h (Planck constant), e (electronic charge), me (electron mass), and mn (neutron mass) must be precisely what they are in order for our Universe to contain those elements (e.g., carbon, nitrogen, iron, etc.) that are essential for life to exist on this planet [165, 166, 212]. Deviations by even a few percent from these values have been shown to lead to alternate model universes devoid of carbon atoms, for example. Therefore, it is clear that there is a closure relation between the existence of life (A) in this Universe and the numerical values of the key physical constants (B) that characterize the structure of this Universe: i.e., Without B, no S. Ji. 17

A; or A presupposes B. Thus anthropic principle can be viewed as a species of the principle of closure. See Section 10.3.

3.7 Isomorphism between Cell and Human Languages

Human and cell languages obey a common set of semiotic principles, including double articulation, the energy requirement of information transduction, storage, and transmission [1, 4]. Human language can be defined as a system of signs obeying a set of rules that enables humans to communicate with one another. In other words, human language is a necessary condition for human communication. Similarly, there must be a language unique to living cells in multicellular [1-5] as well as unicellular [191] organisms, since cells too must communicate among themselves in order to survive by carrying out their specialized biological activities in a coordinated manner. Such a language was named ‘cell language’ in 1997 [1]. Both human and cell languages can be treated as 6-tuples, {L, W, S, G, P, M}, where L is the alphabet, W is the lexicon, S is an arbitrary set of sentences, G is a set of rules governing the formation of sentences from words (called the first articulation) and the formation of words from letters (the second articulation), P is a set of physical mechanisms necessary and sufficient to implement a language, and finally M is a set of objects or processes, both symbolic and material, referred to by words, sentences, and their higher-order structures (e.g., texts). In Table 3, cell and human languages are compared with respect to these components of the linguistic 6-tuple. Table 3 introduces two important concepts, conformons and IDSs, which play fundamental roles in the Bhopalator model of the living cell [38, 56, 57], as will be discussed in Sections 5.1 and 6.2.

Table 3. A comparison between human and cell languages.

Human Language Cell Language Alphabet (L) Letters 4 Nucleotides (or 20 Amino acids) Lexicon (W) Words Genes (or Polypeptides) Sentences (S) Strings of words Sets of genes (or polypeptides) expressed (or synthesized) coordinately in space and time dictated by DNA folds1 (cell states). Grammar (G) Rules of sentence The physical laws and biological rules formation mapping DNA sequences to folding patterns of DNA (polypeptides) under biological conditions2. Phonetics (P) Physiological structures Molecular mechanisms responsible for and processes underlying information and energy transfer and phonation, audition, and transduction driven by conformons3and S. Ji. 18

interpretations, etc. intracellular dissipative structures (IDSs)4. Semantics (M) Meaning of words and Gene-directed cell intracellular processes sentences First Formation of sentences Organization of gene expression events in Articulation for words space and time through non-covalent interactions5 between DNA and proteins (or Space- and time-dependent non- covalent interactions among proteins or among proteins, DNA and RNA molecules). Second Formation of words from Organization of nucleotides (or amino Articulation letters acids) into genes (or polypeptides) through covalent interactions6. Third Formation of texts from Organization of chemical concentration Articulation sentences gradients in space and time (also called dissipative structures [49, 50], both intracellularly and intercellularly7.

1Just as verbal sentences (as written) are strings of words arranged linearly in the geometric space, so the cell-linguistic (or molecular) sentences are visualized as series of gene expression events arranged in time. 2Of all the folds of DNA and polypeptides allowed for by the laws of physics and chemistry, only small subsets have been selected by evolution (thereby giving rise to biological information) to constitute the grammar of cell language. 3Sequence-specficific conformational strains that carry both free energy (to do work) and genetic information (to control work) have been referred to as conformons [32, 34]. Conformons are thought to provide immediate driving force (or serves as the force generators) for all molecular processes inside the cell [34] (see Section 5.1). 4Space- and time-specific intracellular gradients of ions, biochemicals, and mechanical stresses (e.g., of the cytoskeletal system) that serve as the immediate driving forces for all cell functions on the microscopic level have been referred to as intracellular dissipative structures [56, 57] (see Section 6.2). 5Also called “conformational” interactions which involve no breaking nor forming covalent bonds and depend only on the rotation around, or bending of, covalent bonds. Non-covalent interactions implicate smaller energy changes (typically around 1-3 Kcal/mole) and those of covalent interactions entail much larger energy changes (10-50 Kcal/mole) (see Appendix II). 6Molecular interactions that involve changes in covalent bonds, i.e., changes in valence electronic configurations around nuclei of atoms in molecules (see Appendix II). 7This row is an addition to the original table published in [1, 2]. Intercellular communication through chemical concentration gradients is well established in microbiology in the phenomenon of quarum sensing [191], whereby bacteria turn on a set S. Ji. 19 of genes only if there are enough of them around so that they can coordinate their efforts to accomplish a common task which is beyond the capacity of any individual bacteria. The concentration gradients of neurotransmitters established in the synaptic gap between neurons during information flow in neural networks can be viewed as another example of third articulation in cell language.

3.8 The ‘Table Theory’

It may be that we can know an unfamiliar object only in terms of what we already know, reminiscent of the biological principle that organisms derive from preceding organisms (except when life first originated). We may state this idea as follows:

Our knowledge about an unknown object can be increased only in terms of (9) the properties of an already familiar object.

Statement (9) may be related to Socrates’ (ca 470–399 B.C.) doctrine of recollection, or anamnesis, that knowledge can only come from recollection. A similar idea was advanced by the French phenomenologist Maurice Merleau-Ponty (1907–1961) in his theory of ‘pre-reflective experience’ [29, pp. 1-2]. An intriguing possibility to account for the phenomenon of anamnesis may be opened up by the postulated isomorphism between cell and human languages (Table 3). Because we are made up of cells which are in turn made up of material entities originating in nature, we may already know how cells and nature work by virtue of the communication mediated by cell language between the human brain and its constituent cells, but we may not be able to express this knowledge for the purpose of communication among humans because it is not encoded in human language. To do so, we must convert our innate (or internal) knowledge encoded in cell language (which may be identified with “pre-reflective experience” of Merleau-Ponty) into what may be called the external or objective knowledge expressed in human language, and this postulated process of cell-human language transduction (or translation) may constitute the heart of what is referred to in philosophy as “epistemology”. We may represent this series of ideas diagrammatically as follows:

Epistemology

║

[Innate Knowledge encoded in Cell Language]

↓

[External Knowledge expressed in Human Language]

Figure 7. Epistemology as the study of the rules governing the conversion of cell- linguistic texts into human-linguistic text, and as a cell language–based interpretation of S. Ji. 20

Socrates’ doctrine of anamnesis and of the theory of prereflective experience of Merleau- Ponty [29, pp. 1-2]. The language mediating the communication between cells (C) and humans (H) may be referred to as the CH language, distinct from human language (which may be called the HH language) and cell language (the CC language). Through CH language, humans may be able to communicate with the Universe itself, since cells are an embodiment of the laws of the Universe.)

In 1991 [38], I formalized the essence of the above ideas under the rubric of ‘table theory’. The term ‘table’ is employed here, because the theory utilizes a 2-dimensional table as an essential graphical tool for comparing the properties of a familiar (F) object with those of an unknown or unfamiliar (U) object. The table theory has three key elements – 1) a set of components or nodes for F or U; 2) two kinds of relations, internal (IR) and analogical relations (AR) (IR and AR may also be referred to as “intrasystem” and “intersystem” relations, respectively); and 3) the principle of table symmetry stating that, if F and U are isomorphic (or obey a common set of principles), IR of U can be inferred from the IR or F given that AR exists between the components of F and U. These ideas are summarized in Table 4. The main objective of comparing two objects, F and U, is to discover the relations among the components of U (i.e., the vertical arrows among the u’s; see the question mark next to the vertical double arrow in Table 4) by using the relations among the components of F based on the assumption that a set of similarity relations (see the horizontal arrows) holds between components of F and the corresponding components of U.

Table 4. A formalization of analogical inference.

Parameters F (Familiar) U (Unfamiliar)

1) Components: f1, f2, f3, . . . , fn AR u1, u2, u3, . . . , un f1 <------> u1 IR ↨ ↨ 2) Relations: f2 <------> u2 ↨ : Internal Relation (IR) ↨ ↨ (?) <---> : Analogical Relation (AR) f3 <------> u3 ↨ ↨ 3) The ‘Table Symmetry Principle’: . . 1) If F and U are isomorphic, . . 2) If IR(fi-fi+1) is known, . . 3) If AR(fi-ui) is known, and ↨ ↨ 4) If AR(fi+1-ui+1) is known, then fn <------> un 5) IR(ui-ui+1) = IR(fi-fi+1).

The cell language theory (see Table 3 in Section 3.7) formulated on the basis of the similarity between the properties of living cells and those of human language may be viewed as one of the most concrete examples of the application of the ‘table theory’. S. Ji. 21

3.9 The Generalized Franck-Condon Principle

This principle is a generalization of the Franck-Condon principle well-known in the chemical kinetics literature [58]. It is also called the Principle of Slow and Fast Processes (PSFP) [38, p. 52-56], which states that

“Whenever an observable process, P, results from the coupling of two (10) partial processes, one slow (S) and the other fast (F), with F proceeding faster than S by a factor of 102 or more, then S must precede F.”

So generalized, the Franck-Condon principle can be applied to biological coupled processes ranging from ligand binding to proteins and other biopolymers, enzymic catalysis, morphogenesis, and brain functions to the biological evolution itself (see Table 1.12 in [38]). One interesting consequence of PSFP as applied to enzymic catalysis is that enzymes must undergo conformational changes before substrates can bind to their active sites to initiate catalysis, a conclusion diametrically opposed to the induced-fit hypothesis of Koshland widely discussed in biochemistry textbooks (for example, see Figure 8.10 on p. 200 in [59]). It may be convenient to refer to the PSFP-based mechanism of the substrate-enzyme interactions as the “pre-fit hypothesis” to contrast with the induced-fit mechanism, “pre-fit” because enzymes are postulated to have been selected by evolution (and hence carry genetic information) on the basis of their ability to assume certain conformations capable of ‘capturing’ elusive substrates as they bump into them through Brownian motions or thermal fluctuations. These two contrasting mechanisms of binding may be schematically represented as shown in (11) and (12), where L stands for a ligand, E and E’ refer to the two different conformations of the enzyme molecule, the former in the ‘unbound’ and the latter ‘binding conformations’. (The concept of conformations, in contrast to that of configuration, plays an important role in my theoretical reasoning in molecular biology. For a detailed discussion of this topic, see Appendix I). Induced Fit Hypothesis (IFH): L + E ↔ L∙E ↔ L∙E’ (11)

Pre-Fit Hypothesis (PFH): L + E ↔ L + E’ ↔ L∙E’ (12)

The following differences between these two mechanisms should be noted:

1) IFH predicts that E cannot assume E’ without first binding L. In contrast, PFH predicts that E can assume E’ in the absence of L, the probability P of observing E’ being dependent only on the Gibbs free energy difference, G, between E and E’, obeying the Boltzmann distribution law [60, p. 621; 61, p. 33],

P(E’) = P(E) e-G/RT (13)

where R is the universal gas constant and T the absolute temperature. S. Ji. 22

2) The conformational change of E to E’ follows ligand binding in IFH, whereas it precedes ligand binding in PFH. 3) The energy required for the conformational transition from E to E’ is provided by the substrate binding energy in IFH, whereas it is ‘borrowed’ temporarily from thermal environment to be ‘paid back’ subsequently from the free energy of binding of L to E’ rapidly enough to avoid violating the second law [33, pp. 29-30]. This mechanism is consistent with the theory of enzymic catalysis proposed by Jencks [62] based on the concept of Circe effect, in which a part of the free energy of substrate binding is stored in the enzyme-substrate complex as conformational deformations (or strains) of the enzyme to be utilized later to lower the activation free energy barrier for catalysis [62]. 4) Another way to describe the difference between IFH and PFH is that, in the former, L ‘instructs’ E to change its conformation to E’, while, in the latter, L selects’ E’ over E which are both available to L due to thermal equilibrium between the two conformers. Thus, IFH is based on instruction, whereas PFH is rooted in selection.

An indirect evidence for PFH was recently reported by K. Ravindranathan in collaboration with R. Levy [63] who used X-ray crystallographic data on the ribose binding protein (RBP) to calculate the probabilities of observing the ‘open’ and ‘closed’ conformations of this protein in the absence of ribose. (RBP is involved in the ribose transport across the plasma membrane in E. coli.) They found that RBP can exist in closed and open conformations in 4% and 96% of the time, respectively, in the absence of ribose. Most interestingly they find that ribose can bind only to the closed form, thereby shifting the equilibrium toward the closed conformation. Their findings can be represented schematically as follows:

RBPopen ↔ RBPclosed (14)

Ribose + RBPclosed ↔ Ribose∙RBPclosed (15) Processes (14) and (15) can be accounted for by PFH in terms of the differential kinetic behaviors between RBP(slow) and ribose (fast) molecules due to their size difference, which entails the requirement that, in order for ribose to bind to RBP, the slower conformational changes of RBP must precede the faster thermal motions (i.e., collisions) of ribose against the binding site of RBP. Many hormones (including cytokines) exert their biological actions on cells by binding to their target receptors which undergo dimerization. Biologists have been assuming that hormone binding “induces” dimerization of receptor monomers, R, most likely because of the influence of the induced fit hypothesis [59]:

R + H ↔ R∙H + R ↔ R∙H∙R → Biological Actions (16) In contrast, PFH would suggest the following alternative mechanism:

R + R + H ↔ R∙R + H ↔ R∙H∙R → Biological Actions (17) S. Ji. 23

In other words, PFH predicts that receptors can (and indeed must) dimerize before hormones can bind, again for the same kinetic reason as indicated above: The movement of R’s are so slow relative to that of H that, unless R’s are already brought close enough to each other via thermal (or Brownian) motions, H could not be ‘captured’ before it bounces back off from R into the surrounding medium. The X-ray crystallographic investigations on erythropoietin receptor (EpoR) provides another evidence for PFH. EpoR is the receptor for EPO, a glycoprotein (i.e., a protein covalently linked to sugar residues) that, upon binding to EpoR, regulates the proliferation, differentiation, and maturation of red blood cells. EPOR was thought to be activated by EPO-induced dimerization, but the X-ray structural data on the extracellular domains of EpoR, known as the EPO binding protein (EBP), have indicated that EpoR can form a dimmer in the absence of EPO [64], indirectly supporting Process (17) predicted by PFH.

3.10 The Pragmatic Maxim

According to the pragmatic maxim of Peirce, the meaning of a word or a concept can be equated with the totality of the practical effects or consequences that the word has in life: “In order to ascertain the meaning of an intellectual conception (18) one should consider what practical consequences might conceivably result by necessity from the truth of that conception; and the sum of these consequences will constitute the entire meaning of the conception” [13, p. 153.].

This maxim will be applied to defining the meaning of life in Section 4.1.

3.11 The Taoistic Maxim

S. Ji. 24

The Lao Tzu, the classic in Taoist philosophy, is traditionally ascribed to Lao Tzu, an older contemporary of Confucius (551 - 479 B.C.). It is a book of poems consisting of 81 austere chapters, each about 80 Chinese characters- long [193]. The book begins with the 16- character verse shown in Figure 8, which, when translated into Korean, reads:

”Doh Gah Doh, Bee Sahng Doh; Myong Gah Myong, Bee Sahng Myong”.

One English translation of the verse is as follows: “The reality once represented is no longer the ultimate reality. Figure 8. The first 16 characters of The name once named the Lao-tzu embodying the essence is no longer the eternal name.” of the Taoist philosophy [193]. (19)

To capture the content of Statement (19), I propose what may be referred to as the Taoistic maxim (in analogy to the pragmatic maxim defined in Section 3.10 ), which can be stated in more than one ways: “The ultimate reality is ineffable.” (20) “The ultimate reality is unknowable.” (21) “The ultimate reality cannot be completely represented.” (22) “All representations of the ultimate reality are incomplete.” (23) “There are more than one ways to represent the ultimate (24) reality, all equally valid.” .

3.12 The Principle of Self-Organization

The phenomenon of spontaneous generation of the spatial patterns of chemical concentration gradients was first observed in a purely chemical system in 1958 [49, 50] and inside the living cell in 1985 [95] (see Figure 20). These observations demonstrate that, under appropriate initial and boundary conditions, it is possible for chemical concentration gradients to organize themselves in space and time (e.g., oscillating S. Ji. 25 chemical concentrations in test tubes), driven by the free energy released from the chemical reactions themselves. Such phenomena are referred to as self-organization and physicochemical systems exhibiting self-organization are called ‘dissipative structures’ [49, 50]. All living systems, from cells to multicellular organisms, to societies of organisms and to the biosphere, are dissipative structures. The antonym for ‘self- organization’ may be suggested to be ‘other-organization’. ‘Other-organized’ systems include paintings, written texts, and buildings.

3.13 The Principle of Dynamic Balance between Production and Degradation (PDBPD)

The concentration of most, if not all, of the material components of the cell are under constant control or regulation. Cells regulate the intracellular concentrations of cellular constituents at any given time by balancing between the rates of their production (or activation) (RP) and those of their degradation (or deactivation) (RD). Therefore, the concentrations of cellular constituents are actively maintained at one of the three dynamic states – i) dynamic steady states (when RP = RD), ii) on-the-way-up states (when RP > RD), and iii) on-the-way-down states (when RP < RD). In general, RP and RD are space- and time-dependent, so that the concentrations of cellular constituents are similarly time- and space-dependent, thereby qualifying them as members of dissipative structures [49, 50] and hence identifiable with IDSs (intracellular dissipative structures; see Section 6.2). In other words, IDSs are the results of the PDBPD in action.

3.14 The Principle of Intracellular Dissipative Structures (PIDS)

According to the Principle of Dynamic Balance between Production and Degradation (PDBPD) discussed above, all of the cellular components that are under control and regulation are dynamic structures known called IDSs [56, 57]. In other words, PDBPD specifies the mechanisms by which IDSs are produced in cells. The principle described in this section specifies what the function of IDSs is: IDSs constitute the immediate causes for all cell functions [56, 57]. In other words, IDSs and cell functions are synonymous: IDSs may be viewed as the internal (or endo) aspects and cell functions as the external (or exo) aspects of the living cell.

3.15 The Principle of Bioactivity Coefficients

The molecular components of the cell may exist in two distinct states -- active and inactive. For example, genes are inactive when they are buried deep inside chromosomes and active only when they are unpacked and brought out onto the surface so as to interact with proteins located outside chromosomes. Another example would be mRNA molecules that are bound to other molecules that prevent their actions; only when they are freed from such inhibitory ligands would they become active. In analogy to the concept S. Ji. 26 of activity coefficients used in physical chemistry [60, pp. 192-195], it may be convenient to define ‘bioactivity coefficient’, β, as follows:

βi = Ca,i/(Ca,i + Ci,i) = Ca,i/Ct,i (24a)

th where βi is the bioactivity coefficient of the i component of the cell, Ca,i is the th concentration of the active form of the i component, Ci,i is the concentration of the ith th component in its inactive form, and Ct,i is the total concentration of the i component. Therefore, the active or effective concentration of the ith cell components is given by

Ca,i = βiCt,i (24b)

The mechanisms by which a component of the cell is activated or inactivated include (i) covalent mechanisms (e.g., post-transcriptional modifications such as phosphorylation, methylation, acetylation, formylation, protonation, reduction, oxidation, etc.) and (ii) noncovalent mechanisms (e.g., conformation changes, ligand binding, etc.) An indirect experimental evidence for the action of this principle is discussed in Figure 22.

3.16 The Principle of Rule-Governed Creativity

One of the most important principles to be imported into biology from linguistics is the so-called ‘rule-governed creativity’, referring to the fact that the human brain is endowed with the capacity to generate (and understand) an indefinitely large number of meaningful sentences constructed on the basis of a finite number of words and grammatical rules [128]. Consequently, sentences embody two diametrically opposed properties – the rule-governedness due to their compliance to grammatical rules and the creativity associated with the unpredictability of the sentences uttered by a person from one moment to the next. In general, physical and formal systems may be divided into three classes – i) rule-governed (or deterministic) systems (e.g., harmonic oscillators), ii) deterministically chaotic systems (e.g., the Henon-Heiles system [213, p. 39]), and iii) rule-governed creative systems (e.g., natural language, protein folds [1]). Living systems belong to the last class [1].

3.17 The Principle of Recursivity

Many of the above principles are mutually inclusive and intertwined in the sense that it is impossible to separate them completely. This principle is diagrammatically represented in the familiar symbol of the Taoist philosophy: the dot of the Yin is embedded in the sea of the Yang and the dot of the Yang is embedded in the sea of the Yin. Hofstadter discussed this principle in depth using the language of computer science in [65].

S. Ji. 27

Part II Molecules

4 What Is Life?

4.1 Definition of Life

Consistent with the pragmatic maxim (see Section 3.10), I here propose that “There are three aspects to the concept of life: Life as is, life as experienced, and life as theorized.” (25)

In addition to the pragmatic maxim mentioned above, which led to the philosophy of pragmatism popularized by William James [12], Peirce made another major contribution to modern philosophy by formulating his triadic metaphysical doctrine that all phenomena in the Universe comprise three fundamental elements which he called Firstness, Secondness, and Thirdness [13, pp. 85-95; 66]. It is important to keep in mind that Peircean categories are ordinal, not cardinal. That is, Firstness can exist all by itself, but Secondness cannot exist without Firstness, and Thirdness cannot exist without Firstness and Secondness. We may refer to this concept as the ordinality of the Peircean categories and represent it diagrammatically as follows:

The Ordinality of Peircean Categories  Firstness -> Secondenss ->Thirdness (26) where the notation “ A -> B” has the same meaning as defined in the legend to Figure 3, namely, “B cannot exist without A”, or “B presupposes A”. Statement (26) may be made to connect logically to the Peircean categories, if the following proposition is accepted:

“’Life as is’ can be identified with Firstness; ‘life as experienced’ (27) with Secondness; and ‘life as theorized’ with Thirdness.”

By combining Scheme (26) and Statement (27), it may be inferred that no theory of life is complete without containing elements of Firstness, Secondness, and Thirdness. The theory of life formulated in this article contains all these elements: i) Firstness = Life is intrinsic to gnergy (see Section 10.6), ii) Secondness = Gnergy is actualized into Energy/Matter on the one hand and Information/Life on the other, and iii) Thirdness = The Universe possesses the cosmolaguage which is manifest as particle language, cell language, and human language as the Universe evolved (see Section 10.7).

4.2 Life according to SchrÖdinger

It may be said that the first physical theory of life was formulated in 1945, when Schrödinger published his epoch-making book, What Is Life? [67]. This book had a major influence in inducing many talented physicists to enter the field of biology after the World War II, ushering in the era of molecular biology [68], but the book is not without S. Ji. 28 some theoretical deficiencies as pointed out by Perutz [69]. For example, one of the conclusions that Schrödinger arrived at in this book states that

“Organisms feed on negative entropy.” (28) where “negative entropy” is synonymous with “negative entropy change”. This statement must be judged incomplete, if not totally erroneous, because organisms, being open systems, feed on free energy changes rather than on entropy changes alone. Free energy is a function of both energy and entropy [50]. Hence, we can replace Statement (28) with a more accurate statement:

“Organisms feed on free energy.” (29)

4.3 Life according to Prigogine

Ilya Prigogine (1922-2004) divides structures in the Universe into two fundamental classes – equilibrium and dissipative structures [49, 50]. The former includes tables, chairs, rocks, molecules, etc. that can exist without any dissipation of free energy, and the latter is exemplified by the flame of a candle (Figure 9), the Belousov- Zhabotinsky reaction (Figure 10), the living cell (Figure 20), and societies of organisms, etc., all of which require continuous dissipation of free energy to maintain their existence.

S. Ji. 29

Figure 9. The flame of a candle as a dissipative structure supported by the oxidation of the hydrocarbons (i.e., wax) at the wick. Left: Actual flame. Right: The space-dependent physicochemical processes that constitute the flame (A courtesy of Scientific American.).

Like the Belousov-Zhabotinsky reaction run in a petri dish (see Figure 10 below), the flame of a candle is a self-organizing chemical reaction-diffusion system, referred to as a dissipative structure by Prigogine [49, 50]. Different reactions characterized by transient chemical intermediates with different colors occur in different regions in space forming dynamic structures whose boundaries can move when perturbed. One of the major differences between the self-organizing chemical reaction-diffusion processes occurring in the candle flame and those proceeding in the living cell is that the former occurs at high temperatures and the latter at relatively low temperatures (made possible by the presence of enzymes). Therefore, the living cell may be said to support a cold flame vis-à-vis the hot flame of a candle.

Figure 10. The Belousov-Zhabotinsky reaction [49]. The Belousov-Zhabotinsky (BZ) reaction was discovered by a Russian chemist Belousov in 1958 and was subsequently confirmed and further elaborated on by Zhabotinsky. The BZ reaction involves the oxidation of citrate or malonate by potassium bromate, KBr03, in acidic medium, in the presence of the redox pair, Ce3+/Ce4+, which acts as both a catalyst and an indicator dye (Ce4+ being pale yellow, while Ce3+ is colorless). S. Ji. 30

The BZ reaction is characterized by the organization of chemical concentration gradients in 2- and 3-dimensional space and in time (e.g., oscillating concentrations). The dynamic spatial patterns of chemical concentrations can evolve with time. ‘Patterns of chemical concentrations’ are synonymous with ‘chemical concentration gradients’. The organization of chemical concentration gradients in space and time in the BZ reaction is driven by the exergonic (i.e., free-energy-releasing) chemical reactions whose details have been worked out by chemists as shown in Figure 11. Figure 11 shows a more detailed mechanism of the oxidation and reduction (redox) reactions involved in the BZ reaction.

Figure 11. Physical chemists have elucidated the three major groups of the chemical reactions (enclosed in boxes) that are coupled to produce the self-organizing behaviors of the Belousov-Zhabotinsky reaction [49].

So, as Prigogine told me while I was visiting him at the University of Texas at Austin in the early 1980’s,

“Cells are dissipative structures.” (30)

As a corollary to Statement (30), we may logically attribute the following generalization to Prigogine as well:

“Organisms are dissipative structures.” (31)

If we can encapsulate Schrödinger’s theory of life with Statement (28), so can we express Prigogine’s theory of life in terms of Statement (31). Whereas the validity of Statement (28) is debatable, as indicated above, that of Statement (31) is certain. Theoretical statements about life such as (29), (30) and (31) that are based solely on the laws of physics and chemistry cannot be considered complete, since such statements lack the Thirdness aspect of life, namely, the genetic information produced by biological evolution. Thus it may be asserted that:

S. Ji. 31

“No theory of life is complete without incorporating the (32) irreducible triad of entropy, energy, and genetic information.”

The theories of life expressed by Schrödinger and Prigogine in Statements (28) and (31), respectively, are primarily concerned with the energetic (i.e., thermodynamic and chemical kinetic) aspect of life and offer little or no guidance as to the informational (or evolutionary) aspect of life. But, since the discovery of the DNA double helix by Watson and Crick in 1953, an enormous amount of experimental data have accumulated in molecular and cell biology, which any purported theory of life must take into account and provide a rational explanation for. From this perspective, Statements (28) and (31) above must be deemed lacking. One of the primary objectives of this contribution is to propose a theory of life that is consistent with Statement (32), thereby filling the theoretical gap left behind by Schrödinger and Prigogine.

4.4 Life according to Pattee

By extending the earlier theoretical work of J. von Neumann on self-replicating automata [70], H. Pattee formulated what he referred to as the matter-symbol complementarity, according to which all self-replicating and evolving systems in nature possess two complementary aspects – the physical law-governed material/energetic aspect and the evolutionary rule-governed symbolic aspect [44, 71, 72]. As pointed out by von Neumann, there are in principle two ways of accomplishing self-replication – by self-inspection followed by copying and assembling the copied parts, and by self- description in terms of symbols (and more generally in terms of signs as defined by Peirce) followed by executing the resulting instructions to self-replicate. Of these two possibilities, von Neumann concluded, without providing any proof, that the direct copying method would be too inefficient and that the second symbolic method was preferred. Pattee agreed with von Neumann and developed his original idea into the matter-symbol complementarity. Pattee’s concept may be schematically represented as in Scheme (33):

Matter + Symbol ---> Functions (33) where functions include self-reproduction. Scheme (33) is consistent with what is actually found in all living systems, for example, the DNA-based mechanism of self-reproduction. But Pattee and von Neumann, perhaps not being chemists, did not propose any realistic molecular mechanisms for connecting matter-symbol complementarity to functions.

4.5 Life based on the Principle of Information-Energy Complementarity

The theory of life that I have been led to advocate since the early 1970’s is based on the notion of the information-energy complementarity, according to which all goal- directed, self-organizing chemical reaction-diffusion systems in the Universe, including S. Ji. 32 living systems are ultimately driven by a complementary union of information and energy. The third entity for which information and energy are its complementary aspects was named gnergy in the mid-1980’s [38, 56]. Discrete physicochemical entities carrying gnergy are called gnergons which produce heat upon realizing their associated goal-directed processes, i.e., functions. Therefore, it follows that there is a one-to-one correlation between gnergons and their conjugate functions, as indicated by the double arrow in Scheme (34):

Ergons ^ Gnons ---> Gnergons <----> Functions (34) where the symbol ^ indicates the complementary relation between ergons (e.g., mechanical energy) carrying energy and gnons (e.g., amino acid sequences of catalytic domains of proteins) carrying information. Schemes (33) and (34) reveal both the similarities and differences between Pattee’s theory and the theory of life presented here. As already noted, Pattee did not specify any realistic molecular mechanism of coupling matter-symbol complementarity to functions. In contrast, Scheme (34) invokes the intermediate entity gnergons as a means to couple information and energy to functions. The molecular mechanisms responsible for generating gnergons (of which there are two known examples, conformons and IDSs) will be discussed in Sections 5.2 and 6.2. But it is important to note that, in Scheme (34), gnergons and functions are best viewed as synonymous and thus represent two sides of the same reality: i.e., Gnergons and functions exhibit the Möbius relation (see Section 3.5) and possibly other relations as well, including the principles of closure (Section 3.6) and recursivity (3.12). Or it may be that gnergons are the internal (or endo) view of the cell, whereas cell functions are its external (or exo) view of the living cell.

5 The Conformon Theory of Molecular Machines and Motors

5.1 Conformons: Experimental Evidence

As already alluded to above, conformons are defined as the mechanical energy stored in deformable biopolymers at sequence-specific sites providing the free energy (or force) for driving goal-directed molecular processes in the cell [31-35]. They were first invoked to explain the molecular mechanisms underlying free energy transfer from one protein to another in mitochondria during energy-coupled process known as oxidative phosphorylation [32]. During oxphos, the enzyme systems located in (and on) the inner mitochondrial membrane synthesize ATP (the energy currency of the living cell) from ADP and inorganic phosphate, Pi, using the free energy derived from the oxidation of NADH to NAD+. The whole process is very complex and has not yet been completely elucidated in my opinion [33], despite the fact that biochemistry textbooks around the world assume that chemiosmosis (i.e., conversion of chemical energy of NADH to the osmotic energy of the pH gradient across the inner mitochondrial membrane) is responsible for driving the synthesis of ATP from ADP and Pi (e.g., see Figure 21-22 on p. 545 in [73]). One glaring deficiency of the chemiosmotic hypothesis, for which P. Mitchell received the Nobel Prize in Chemistry in 1978, is a complete lack of any S. Ji. 33 enzymologically realistic molecular mechanism that can convert chemical energy of NADH to the osmotic energy of the pH gradient and associated membrane potential. The chemiosmotic hypothesis can be represented as:

Mechanism (?) + NADH + ½ O2 ------> NAD + H2O + Proton gradient (35)

Proton gradient ADP + Pi ------> ATP + H2O (36)

To provide a chemically realistic molecular mechanisms underlying energy conversion in (35), an alternative mechanism of oxphos, known as the conformon hypothesis, was formulated in 1972 [31-34, 56, 57, 74], according to which the free energy conversion involved proceeds through three main steps:

ETC‡ + NADH + ½ O2 + ETC ------> NAD + H2O + ETC* (37)

(ETC/TRU) ‡ ETC* + TRU ------> ETC + TRU* (38)

TRU‡ ADP + Pi + TRU* ------> ATP + H2O + TRU (39) where all the macromolecular systems (or machines) are written in bold letters, ETC stands for electron transfer complexes (of which there are three, denoted as I, III, & IV) located in the inner mitochondrial membrane, and TRU is an abbreviation for “tripartite repeating unit”, the enzyme system consisting of (i) F0, acting as the proton channel, (ii) the oligomycin-sensitivity conferring protein (OSCP) acting as the stalk connecting F0 and F1, and (iii) F1, also called the ATP synthase or Complex V that catalyzes the phosphorylation of ADP to synthesize ATP (see Figure 1 in [56], and Figure 16 below). Notice that, in each step, the enzyme system involved plays a dual role – as a carrier of free energy denoted by the superscript * symbol and as an enzyme lowering the energy level of the transition state denoted by the superscript ‡ symbol. Thus, a significant amount of the free energy generated from the oxidation of NADH is stored in ETC* in Step (37), which is then transferred to TRU* in Step (38), which finally drives the free energy-requiring desorption of ATP from F1 (194). Thus, according to the conformon hypothesis of oxphos, every key step in oxphos occurs inside the inner mitochondrial membrane and at no time is there any transmembrane proton gradient formed: No chemiosmosis is required for oxphos. However, the free energy stored in TRU* can be utilized to generate transmembrane proton gradient, if necessary, given appropriate experimental or physiological conditions, when the energy is transferred from TRU* to a hypothetical enzymic unit called the “proton transfer complex”, PTC, yet to be discovered [33, 56, 74]. It has been postulated that the proton gradient formed across S. Ji. 34 the inner mitochondrial membrane is needed not for oxphos (as assumed by Mitchell) but mainly for the communication between mitochondria and the cytosol for the purpose of monitoring the ATP needs of the cell, and possibly for synthesizing ATP driven by the proton gradient generated by anaerobic glycolysis during anoxia (i.e., lack of oxygen) or ischemia (i.e., lack of blood flow) [38, pp. 60-61]. It is further postulated that when this mechanism of proton-mediated intracellular communication breaks down due to the permeability transition of the inner mitochondrial membrane, the cell undergoes a programmed cell death known as ‘apoptosis’ (see Appendix III). The idea that biological properties of enzymes may depend on the mechanical (i.e., conformational) energy stored in proteins was first seriously entertained by R. Lumry and others as early as the 1950’s and 1960’s [75] (reviewed in [32] and [34]), but the first direct experimental evidence for such a possibility did not emerge until the mid- 1960’s when the so-called “supercoiled” DNA was observed under electron microscope [73, p. 795] (see Figure 12 below).

Figure 12. Supercoiled DNA duplexes. (Left) The electron micrograph of two circular DNA duplexes (i.e., two DNA strands intertwined), one supercoiled into a compact shape (upper left) and the other relaxed (lower right) (from p. 795 in [73]). (Right) Three shapes of DNA duplexes – a linear form (left), a circular form with one strand nicked (or cut) (middle), and a circular form that is closed and supercoiled (right) (from p. 124 in [76]).

When a circular DNA duplex is cut through both strands and one of the resulting ends are twisted (while the other end is held fixed) around the long duplex axis (called the helical axis) n times in the direction of increasing the distance between the paired bases (referred to as the negative direction) and then resealed, the circular form twists in space so that the helical axis itself coils into a helix (hence called “supercoiling’). One superhelical turn is associated with breaking 10 hydrogen bonds between the complementary base pairs along the DNA double helix, each hydrogen bond requiring about 1.5 Kcal/mole of free energy to break. Thus, a circular DNA duplex which was S. Ji. 35 negatively twisted around the helical axis, say, 20 times stores approximately 15 x 20 = 300 Kcal/mole of mechanical energy in the form of conformational deformation or strains. Therefore, a supercoiled DNA duplex can be interpreted as providing a direct experimental evidence for the concept of conformons. That is, the supercoiled DNA duplex shown in Figure 12 stores conformons J. H. White derived a mathematical formula (known as White’s formula; see pp. 795-796 in [73]) that specifies the relation among three parameters – (i) the linking number, Lk, the number of times the two strands are intertwined, (ii) twist, Tw, a number determined by the local pitch of the helix (i.e., the distance between two equivalent points along a helical strand), and (iii) writhe, Wr, a number determined by the degree of the twisting of the helical axis in space [76]:

Lk = Tw + Wr (40)

These parameters are explained graphically in Figure 13. Notice that a relaxed circular DNA duplex is characterized by the lack of any writhe, i.e., Wr = 0, and non-zero values for the other two parameters (see B in Figure 13). As described above, writhe can be introduced into the circular DNA duplex by first cutting the two strands of a relaxed form and by turning one of the two ends counter-clock-wise n times before resealing the two ends to regenerate the circular form (see C and D in Figure 13), which can spontaneously be converted into supercoiled form (see E in Figure 13). It is important to note that, Lk can be altered only through the cutting-twisting-resealing operation (as described in B, C and D in Figure 13), which are efficiently carried out by ATP- dependent enzymes known as topoisomerases or DNA gyrase, and that the remaining two parameters, Tw and Wr, can change in a mutually compensating manner, as shown in D and E. If the linking number of a relaxed circular DNA duplex is Lk0 and the corresponding number for a supercoiled circular DNA duplex is Lk, then the linking number difference (usually denoted as ) can be expressed as:

 = Lk - Lk0 = (Tw + Wr) - (Tw0 + Wr0)

= (Tw - Tw0) + (Wr - Wr0)

= ΔTw + ΔWr (41)

Inside the cell, DNA molecules are commonly maintained by topoisomerases in negatively supercoiled states, making their linking number Lk smaller than their relaxed values Lk0 so that  = Lk - Lk0 < 0. Therefore,  can be interpreted as a quantitative measure of conformons embedded in circular DNA [34]. Applying Equation (41) to the DNA supercoils shown in Figure 13 leads to the numerical results displayed in Table 5. As already indicated, linking number difference  can be viewed as a quantitative measure of the free energy stored in supercoiled DNA introduced by nicking-twisting-resealing operation on the circular DNA duplexes. Interestingly this mechanical energy can be distributed either in the twist (ΔTw) or write (ΔWr) of the supercoiled DNA duplex (see the last row in Table 5). The former represents the mechanical energy stored in local deformation (see D in Figure 13), while S. Ji. 36 the latter indicates the same energy distributed over the whole circular DNA duplex (see E in Figure 13). These results support the concept of conformons as mobile mechanical energy stored in bioploymers.

Table 5. Mechanical energy stored in circular DNA duplexes. Data from Figure 13.

Conformational or Energy State Lk Tw Wr of Circular DNA Duplexes Ground: Relaxed Circle (B) 25 25 0 Energized: Unwound Circle (D) 23 23 0 Energized: Negatively Supercoiled (E) 23 25 -2  = ΔTw + ΔWr ΔTw = -2 ΔWr = 0  = -2 (Δ = Energized State – Ground State) ΔTw = 0 ΔWr = -2

The last row of this table indicates that the conformons (as measured by ) can be either localized (see ΔTw = -2, as in D in Figure 13) or distributed over the whole circular DNA duplex (see ΔWr = -2, as in E in Figure 13).

Figure 13. The definition of the three terms appearing in White’s formula for supercoiled S. Ji. 37

DNA. Lk = linking number, Wr = writhe, and Tw = twist. The mathematical equation connecting these terms was obtained by J. H. White [76]. See Eq. (40) above.

In the early 1990’s, C. Benham developed a statistical mechanical formalism to describe the dynamics of the mechanical strains introduced in circular DNA duplexes [77, 78, 195]. His computational results indicated that the so-called “stress-induced duplex destabilizations (SIDDs) (equivalent to  < 0 in Table 5) were not randomly distributed along the circular DNA duplex but were confined predominantly in the 5’ and 3’ ends of RNA coding regions. Three examples of SIDDs are shown in Figure 14 (see the directed arrows), where the down-ward deflections indicate the decrease in the Gibbs free energy of strand separation secondary to the localized destabilization induced by mechanical strains. Thus, both the sequence-specificity and the mechanical energy stored in DNA make SIDDs excellent examples of the more general notion of conformons invoked two decades earlier [32, 34, 79].

Figure 14. Mechanical strains of DNA localized at sequence-specific sites within circular DNA duplexes. The x-axis records the nucleotide positions along the DNA duplex and the y-axis records the Gibbs free energy required to separate the based pairs located at position x along the DNA duplex chain. Notice that the base pairs located near the 3’- end (i.e., the right-hand end of the arrow) of some genes are already completely separated (see position 138.7 in (a) and 3.56 in (b)).

Thus, it is evident that the results obtained by Benham complement the electron microscopic evidence in supporting the concept of conformons in DNA in particular and in biopolymers generally.

S. Ji. 38

5.2 Molecular Mechanisms of Conformon Generation based on the Generalized Franck-Condon Principle

A theoretical mechanism of converting/transducing chemical energy into mechanical energy of conformationally strained enzymes (i.e., conformons) was first proposed in 1974 [32] based on the Franck-Condon principle well-established in physics and chemistry [31, 33, 34]. The molecular strategy for accomplishing the chemical-to- mechanical energy conversion is summarized below. It should be noted that the chemical-to-mechanical energy conversion is synonymous with chemical reaction- induced force generation because force and energy (or work) are related through the second law of Netwonian mechanics, F = m, where F is force, m is mass, and  is acceleration and, by the definition of energy E as the ability to do work, E = Work = Force x Distance. 1) Enzyme E borrows thermal energy from its environment to generate conformational strains (called virtual conformons) localized at sequence-specific loci: E ↔ E’*, where E’ indicates a metastable conformational state of E and the superscript star refers to local conformational strains or virtual conformons entrapped in sequence-specific sites within E’. 2) E’, not E, catalyzes an exergonic chemical reaction, A ---> B. 3) B stabilizes E’*, leading to a partial conservation of the free energy released from the A ---> B reaction as real conformons in E’*. In other words, a part of the free energy released from the A ---> B reaction converts virtual conformons to real conformons. These three steps can be viewed as the molecular mechanism underlying the Circe effect as well that Jencks proposed as underlying enzymic catalysis [62]. They are also consistent with the theories of molecular machines and motors proposed by McClare [196] and Astumians [197, 198].

5.3 The Conformon Hypothesis of Energy-Coupled Processes in the Cell

The cell is composed of three main classes of material entities – biopolymers (i.e., DNA, RNA proteins, etc.), metabolites (e.g., glucose, pyruvate, NADH, ATP, O2, CO2, + + + ++ H2O, etc.) and inorganic ions (e.g., H , Na , K , Ca , etc.). The interior space of the cell is so crowded with these molecular entities that changing the concentration of any one component at a given locus within the cell may affect the chemical activities of others in distant locations due to the so-called “crowding effects” [80]. All these molecular entities are in constant motions under physiological temperatures, and these motions can be divided into three main categories—i) up-hill motions (e.g., ion pumping, molecular motor movement, synthesis of ATP, etc.; also called energy-requiring, or endergonic processes), ii) down-hill motions (e.g., diffusion of ions across a membrane, ATP hydrolysis; also called energy-dissipating or exergonic processes), and iii) random motions (e.g., thermal fluctuations or Brownian motions of biopolymers, collisions among molecules). In order for the cell to carry out its biological functions such as growth, chemotaxis, cell cycle, cell differentiation, and apoptosis (i.e., programmed cell S. Ji. 39 death), evolutionarily selected uphill reactions must be coupled to conjugate down-hill reactions so as not to violate the laws of thermodynamics. Such coupled processes are often referred to as “energy-coupled” processes, meaning that the free energy released from the down-hill reaction is partially ‘transferred‘ to the up-hill reaction in such a manner that the net free energy change accompanying the overall coupled process is still negative. Examples of energy-coupled processes include respiration-driven ATP synthesis (or oxidative phosphorylation), ATP- or respiration-driven active transport of protons across the mitochondrial inner membrane, and ATP-driven molecular motors and rotors. The conformon theory maintains that all such coupled processes proceed through the production and utilization of conformons [34]. This idea can be represented schematically as follows:

(Exergonic Reaction) ----> (Conformons) ----> (Endergonic Reaction) (42)

When Scheme (42) is applied to mitochondria which are known to utilize the free energy released from respiration to drive the synthesis of ATP from ADP and Pi or to pump protons across the mitochondrial inner membrane, the following scheme results:

---> (ATP Synthesis) (43) / /\ / | (Respiration) -----> (Conformons) | \ | \ \/ ---> (Proton Gradient) (44)

The upper branch of the above scheme is the molecular mechanism of oxidative phosphorylation suggested by the conformon hypothesis, which differs significantly from the chemiosmotic hypothesis which in effect replaces conformons in Schemes (43) and (44) with proton gradients, as shown in Scheme (45).

5.4 Deconstructing the Chemiosmotic Hypothesis

The British biochemist, P. Mitchell (1920-1992), proposed the concept of chemiosmosis in 1960 [81, 82] in an attempt to explain how mitochondria (also known as the powerhouse of the cell) synthesize ATP from ADP and inorganic phosphate, Pi, utilizing the free energy changes accompanying the oxidation of substrates (such as NADH) during respiration. His basic idea is that the chemical energy of NADH is first converted into the osmotic energy (hence the adjective ‘chemiosmotic’) of the proton gradient across the mitochondrial inner membrane and associated membrane potential which is subsequently converted back to the chemical energy of ATP: 1 2 (Chemical Energy of NADH) ---> (Proton Gradient) ---> (Chemical Energy of ATP)

(45) S. Ji. 40

where Process 1 indicates the translocation of protons across the mitochondrial inner membrane driven by respiration (see the upper box in Figure 15 below), and Process 2 indicates the proton gradient-driven phosphorylation of ADP to ATP (see the lower box in Figure 15). The key postulates of the Mitchell hypothesis are as follows: 1) The membrane-embedded respiratory enzymes (symbolized by the upper box in Figure 15) somehow separate the electron (indicated by the encircled negative charge) and the proton (H+) from the hydrogen atom (H) and move the former across the membrane (from the left, L, to the right, R, side), leading to the generation of a transmembrane proton gradient and a membrane potential (not shown) and attendant acidification of the L compartment and alkalinization of the R compartment. 2) The osmotic energy stored in the proton gradient (also called the electrochemical gradient of protons or the “proton-motive force”) then (again somehow) drives the abstraction of the hydroxyl ion (OH’) from the L compartment and the proton from the R compartment to effectuate the removal of water molecules from ADP and Pi, leading to the synthesis of ATP, all proceeding at the reaction center embedded inside the M phase (see the dotted circle in the lower box). . 3) The respiratory enzyme system catalyzing Process 1) and the reversible ATPase system catalyzing Process 2) are coupled through the mediation of the common chemical species, i.e., protons, which are generated from respiration (upper box) and consumed by ATP synthase (lower box).

Figure 15. The chemiosmotic hypothesis proposed by P. Mitchell in 1960. Copied from [81]. S. Ji. 41

I have long been a critic of the chemiosmotic hypothesis, because it does not provide any fundamental theoretical insights into the molecular mechanisms underlying oxidative phosphorylation and photophosphorylation. Besides, even if the chemiosmotic hypothesis proves to be theoretically correct, it cannot represent a universal principle of biological energy coupling, because there are membrane-independent (and hence non-osmotic) energy coupled processes in biology as well, including muscle contraction, molecular motors moving cargoes along cytoskeletal tracks, and DNA supercoiling that accompanies gene expression in the nucleus (as already discussed above) [33, pp. 34-35; 38, pp. 60-61] which the Mitchell hypothesis cannot account for. R. J. P. Williams is another critic of the chemiosmotic coupling concept. His criticism, aired from the very beginning of the chemiosmotic conception, is based on the consideration of thermodynamic efficiency [83]. Now let me make more specific criticisms on the chemiosmotic hypothesis:

1) Mitchell’s proposed mechanism for effectuating respiration-driven proton translocation across the mitochondrial membranes is based on what he calls vectorial metabolism or anisotropy of membrane protein organization [81, 82]. This idea seems to me to be insufficient to account for oxidative phosphorylation, because structural organization alone, no matter how asymmetric, cannot give rise to asymmetric distribution of the products of chemical reactions without dissipating any requisite free energy. Without enzymologically realistic mechanisms for coupling a down-hill reaction (e.g., oxidation of NADH to NAD+) to an up-hill chemical reaction or physical process (i.e., vectorial movement of protons and asymmetric removal of water or the hydroxyl group from the ATP synthesis center), structural asymmetry alone cannot accomplish any asymmetric metabolism. In fact to cause a symmetry breaking in molecular processes without dissipating requisite free energy would be tantamount to violating the first law of thermodynamics, because the resulting gradients could be harnessed to do work, thereby creating energy anew2 2) Structural asymmetry alone, without any dynamic molecular mechanisms associated with them, will not automatically lead to asymmetric chemical reaction (i.e., asymmetric abstraction of water molecules, in this case). In other words, structural asymmetry is necessary but not sufficient for effectuating asymmetric metabolism. In addition, it is critical for the chemiosmotic hypothesis that the ATP synthesizing reaction center (see the dotted circle in the lower box in Figure 15) be located within the M phase, not in the R phase, since, in the latter case, the ATP synthesis evidently cannot be driven by any osmotic energy of the transmembrane proton gradient. Recent X-ray crystallographic findings [84, 85] clearly demonstrate that the ATP synthase is located on the R side of the membrane, M (see Figure 16 below), which may be difficult to be accommodated by the chemiosmotic hypothesis. 3) As already alluded to above another evidence against the chemiosmotic hypothesis is the structural data that have just come to light only recently [84, 85], according to which the ATP synthesizing portion of the reversible ATPase (i.e., F1) is not embedded within the mitochondrial inner membrane as Mitchell assumed but located outside the membrane phase attached to the proton pumping S. Ji. 42

structure (i.e., F0) in the M phase through a set of long polypeptide chains (designated as γ and ε subunits) in Figure 16). It is now generally accepted that, when ATP synthase catalyzes the formation of ATP from ADP and Pi driven by the proton gradient, the electrochemical energy of proton gradient is first converted into the mechanical energy (in the form of ‘torque’, i.e., the energy producing a rotatory motion) within F0 [84], which is then transferred to F1 (through the rotatory motion of the shaft composed of the γ and ε subunits), where the energy is utilized to release (or de-bind) ATP from F1. Thus, the sequence of events involved in proton gradient-driven synthesis of ATP can now be described as follows: 1 (Proton Gradient) <----> (Mechanical Energy of the γ and ε Subunits) /\ | | 2 \/ (Chemical Energy of ATP) (46) Reaction (46) is known to be reversible so that protons can be pumped across the mitochondrial inner membrane (producing osmotic energy) using the chemical energy of ATP hydrolysis (see the double arrow in Scheme (43)-(44)). On the phenomenological level, therefore, the concept of chemiosmotic coupling proposed by Mitchell may appear validated since chemical and osmotic energies are indeed interconvertible. But this way of looking at the problem is superficial. The heart of the problem concerns not so much whether or not the process of chemiosmosis can occur in mitochondria (which was known to take place in living systems long before 1960 when the Mitchell hypothesis was formulated) but exactly how such a process can occur on the molecular level. In other words, we must distinguish between the phenomenon of chemiosmosis and the molecular mechanisms underlying chemiosmosis. On the phenomenological level, the Mitchell hypothesis cannot be faulted. But it is on the level of molecular mechanisms of chemiosmosis that the Mitchell hypothesis seems to fail, as I have been trying to point out over the past three decades [33, pp. 34-35; 38, pp. 60-61; see also 83]. As discussed in detail in Section 5.1, any mechanical (i.e., conformational) energy stored in biopolymers can be viewed as examples of conformons. Therefore we can rewrite Reaction (46) as follows: 1 (Proton Gradient) ----> (Conformons Stored in the γ and ε Subunits) | | 2 \/

(Conformons Stored in F1 ↔ Chemical energy of ATP) (47) Process 1 above is the step where conformons are generated from proton gradients, most likely by reversing the molecular steps postulated for the conformon- driven active transport (see Figure 2 in [33]). Process 2 involves conformon transfer from S. Ji. 43

F0 to F1 through the γ and ε subunits, which probably occur through the mechanism of conformon transfer proposed in [32] (see Figure 4 therein). All the theoretical problems faced by the chemiosmotic hypothesis as indicated above and other potential problems (discussed in Appendix III) can be resolved simply by invoking the concept of conformons which can drive either active transport or ATP synthesis as indicated in Scheme (43)-(44), depending on the metabolic needs of the cell. With this scheme, the conformon theory accounts for not only membrane-dependent oxidative phosphorylation and active transport but also membrane-independent muscle contraction, DNA supercoiling, and cytoplasmic molecular motor movements, all through the common currency of the energy and information carried by conformons [32, 33].

Figure 16. The separate localization of the proton-consuming reaction center (see the lower part labeled F0) and the ATP synthesizing system (labeled F1) in the membrane and in the extramembrane phases, respectively, in mitochondria [84, 85].

5.5 The Information-Energy Landscape Theory of Protein Folding

The field of protein folding appears to have gone through a paradigm shift around 1995, largely led by Wolynes and his group and by others [86, 87], fulfilling the earlier theoretical speculations of Harrison and Durbin [88]. The paradigm shift involves replacing the idea of folding pathways with the so-called 'folding funnel' (to be explained below). In other words, the earlier notion of a denatured protein folding to its final native S. Ji. 44 conformation through a series of distinct intermediate conformational states has been replaced by a new view, according to which an ensemble of conformational isomers (often called "conformers", not to be confused with "conformons"; a conformer can carry many conformons in it) of a denatured protein undergoes a transition to a final native conformation through a series of "ensembles" of conformational intermediates, each intermediate following a unique folding path to the final common native structure. In short, the paradigm shift is from individual intermediate conformational isomers of a protein to an ensembles of them, on the one hand, and from a single folding pathway to an ensemble of folding pathways (down the folding funnel), on the other. P. Leopold et al [89] characterize the 'protein folding funnel' as follows: ". . . .a kinetic mechanism for understanding the self-organizing principle of the sequence-structure relationship. This concept follows from a few general considerations. (i) Proteins fold from a random state by collapsing and reconfiguring (i.e., mainly conformationally rearranging polypeptides without breaking or forming covalent bonds; my addition), (ii) reconfiguration occurs diffusively (i.e., as a consequence of Brownian motions of proteins: my addition) and follows a general drift from higher energy to lower energy conformations, and (iii) reconfiguration occurs between conformations that are geometrically similar--i.e., globular inerconversions are energetically prohibitive after collapse-- so local interconversions alone are considered. We define the folding funnel as a collection of geometrically similar collapsed structures, one of which is thermodynamically stable with respect to the rest, though not necessarily with respect to the whole conformation space. . . ." Just as water flows down a funnel, higher energy conformers of a denatured protein are thought to "flow" down the folding funnel toward lower energy conformers through several conformational states ('molten globular states', 'transition state', ‘glass transition', and 'discrete folding intermediates') to the final native structure. The movement of protein conformers down the folding funnel entails two kinds of thermodynamic changes -- i) energy decrease due to downward movement, and ii) entropy decrease due to the narrowing of the funnel width, reflecting increasing conformational constraints (i.e, as conformations of a protein become more compact to minimize energy, the conformational motions of proteins become confined to an increasingly smaller volume, leading to a decrease in entropy). Since protein folding is ultimately driven by Gibbs free energy changes, we have G = E + V - TS (where  denotes the difference between the final and initial states), which becomes G = E -TS, if the pressure-volume work is negligible in protein folding, it would follow that, at some point along the vertical axis of the folding funnel, the free energy decrease, -G, due to energy decrease, - E, should exactly cancel out the free energy increase, +G, due to entropy decrease, -S, so that G = 0. At this point, protein folding process stops and an equilibrium state is reached. The folding funnel theory as now formulated seems to lack a “genuinely biological dimension" indicated in Statement (32), because the theory seems to be based on the fundamental assumption that protein folding is determined by a tendency to minimize free energy (cf., the 'principle of minimal frustrations' [90]) in contrast to the more likely possibility that proteins in living cells have been selected by evolution, not S. Ji. 45 based on free energy minimization, but rather based on their biological functions, regardless of their free energy levels. Their biological functions in turn would be determined by their 3-dimensional molecular shapes. It may be possible to expand the 2- dimensional folding funnel diagram (Figure 1 in [86]), consisting of the y axis encoding energy and the x-axis (i.e., the width of the funnel) encoding entropy, by adding a z-axis perpendicular to the xy-plane to accommodate the effects of biological evolution (i.e., genetic information) on protein folds. Thus it should be possible to incorporate biological evolution into the protein folding theory in this manner. The 'folding funnel' model of protein folding is also called "energy landscape" model. One way to incorporate biological evolution (and hence information) into the energy landscape theory of protein folding may be to identify the topology (i.e., surface shape) of the energy landscape as the medium for encoding the effects of biological evolution. Although I have no proof, it seems to me that there may be a good correlation between the degree of the bumpiness (measured by, say, the number of the bumps and associated valleys of the folding funnel which together serve as the kinetic barriers for entrapping sequence-specific conformational strains, i.e., 'conformons' [34] or 'frustrations' [90]) of the energy landscape and the genetic information encoded in amino acid sequence of proteins. It may be speculated that the bumpier the surface of the energy landscape of a protein, the higher its information content (of the Hartley type [91]). Thus, the notion of "bumpy folding funnel" would embody the following three elements: i) E, energy encoded in the depth of the funnel, ii) S, entropy encoded in its width, and iii) I, genetic information encoded in the 'bumpiness' or 'ruggedness' of the funnel surface. The protein folding theory incorporating these three elements, E, S, and I as described above may be referred to as the "information-energy landscape" theory of protein folding ('entropy' being included as a part of 'energy', an abbreviation for 'free energy') to contrast with the now widely accepted "energy landscape" theory of protein folding. It is my opinion that the "energy landscape theory" of protein folding is a physical theory and not a biological one. To transform the energy landscape theory into a biological theory that is consistent with Statement (32), it is necessary to combine it with a theory of biological evolution (in the form of say a cell model). One such biological theory is the "information-energy" landscape theory outlined here, which can be viewed as a manifestation of the information-energy complementarity principle (see Section 3.1).

5.6 Decoding DNA based on the Semiotic Lessons Learned from Decoding the Rosetta Stone

As reviewed in Section 5.1, DNA can carry not only sequence information but also mechanical energy, both of which are required for executing various goal-directed motions of chromatins (e.g., gene expression) in the nucleus of the cell. The decoding of this duality of information and energy in DNA may have some similarity to the decoding in 1822 of the Rosetta stone by the French Egyptologist, Jean-Francois Champollion (1790-1832). The key to this successful decoding of the Rosetta Stone was the discovery S. Ji. 46 that Egyptian hieroglyphs were not only logograms as had long been assumed but also phonograms as well. Similarly, based on the information-energy complementarity principle, it was concluded that DNA texts are logophonograms serving as logograms externally (at least for the human mind, if not for the cell as well) and phonograms internally (i.e., for the cell) (see Appendix IV). Both Egyptian hieroglyphs and DNA sequences are signs because they stand for something other than themselves. However, there are important differences between these two kinds of signs. For one thing, Egyptian hieroglyphs are macroscopic in size and exist outside the human brain, whereas DNA sequences are microscopic in size and exist inside the human brain as a part of all neurons. Rosetta Stone (Figure 17) was discovered in 1799 by French soldiers during a brief occupation of Egypt by Napoleon and his army [92-94].

Figure 17. The Rosetta stone comprising three texts written in Egyptian hieroglyphs, Demotic, and Greek. S. Ji. 47

The Rosetta Stone contains three scripts inscribed on its surface–Egyptian hieroglyphs that were unknown, demotic and Greek scripts which were known. Most importantly, all the texts written in these scripts had a common object of reference, namely, the decree of Memphis, extolling the accomplishments of a young Pharaoh. Therefore, it was anticipated that the unknown text would eventually be deciphered through comparing it with the known texts. Indeed, Champollion was able to decipher the Egyptian glyphs because of the availability of a common referent expressed in at least one known language [92]. Another key ingredient for deciphering hieroglyphs was the discovery by Champollion of the fact that some hieroglyphs represented both sounds and meanings. That is, they acted as both phonograms (representing sounds) and logograms (representing an idea or information), unlike the Egyptologists before him who thought hieroglyphs were either pure logograms or phonograms. In other words, Egyptian hieroglyphs comprise three distinct groups of signs as shown in Table 6.

Table 6. Three classes of written signs in Egyptian hieroglyphs, extracted from [93] and [94]. The term “logophonogram’ used here may be new. It was coined to indicate a symbolic sign (-gram) with the dual function of carrying meanings (logo-) as well as acting as a phoneme (-phono-). It is suggested here that the molecular analogs of logophonograms are conformons.

Logograms (A) Phonograms (B) ‘Logophonograms’ (C) Iconic signs for a woman and All the hieroglyphic The circle-and-a-dot symbol a boy placed as the end of a signs in the name of appearing in the name of the hieroglyphic name to indicate Cleopatra, Ptolemy, Pharaoh Ramses stands for the sex of the person named. and Alexander. i) the sun called ‘rah’ in Coptic, and ii) the phoneme ‘r’ in Egyptian hieroglyph.

The essential contribution that Champollion made to decoding Egyptian hieroglyphs was his demonstration that they contained both phonograms and logophonograms, first noted in the hieroglyphic name, Pharaoh Ramses. The lesson learned from the decoding of hieroglyphs may serve as a useful model for decoding the human genome viewed as a DNA text. I suggest the analogy shown in Table 7.

Table 7. A possible analogy between the decoding of Egyptian hieroglyphs and the human genome.

A B C Egyptian Logograms Phonograms Logophonograms Hieroglyphs Human Genome Genetic Free Energy Gnergy carried by S. Ji. 48

Information conformons

The situation that prevailed in Egyptology before the breakthrough of Champollion may be comparable to what is happening in the field of the contemporary molecular biology. Most researchers believe that the sole role of DNA is to carry genetic information in the form of nucleotide sequences and hence that a complete sequencing of the genome of an organism will suffice to elucidate the meaning and the function of the DNA text of that organism. In contrast, a small but growing minority of molecular biologists entertain the notion that sequence information alone is not sufficient and the dynamics of DNA and associated proteins must be taken into account to completely understand how DNA works [34, 77, 78]. Even to some of the latter group of molecular biologists, the idea that DNA may carry both genetic information and mechanical energy as an inseparably fused entity (referred to as gnergons or conformons [11, 34, 38]) may seem largely alien. To support the latter possibility, we can compare the Egyptian hieroglyphic text decoded by Champollion and the DNA viewed as a molecular text (see Figures 17 and 18).

S. Ji. 49

Figure 18. The human brain compared to the Rosetta Stone (see Figure 17). Just as Rosetta stone contained two scripts, one known and the other unknown, so it is postulated that the human brain contains a known script (written in human language) and unknown script written in cell language. It is further postulated that these two kinds of scripts share a common set of physical laws and evolutionary rules, thereby exhibiting an isomorphism [1-5]. It is because of this postulated isomorphism between the two kinds of languages that we can hope to infer the semantics of the cell-language texts (see the left-hand box in Figure 18) by comparing them with associated human linguistic texts (see the right-hand box in Figure 18). The key elements of this comparison are summarized in Table 8.

Table 8. A comparison between Egyptian hieroglyphs and DNA texts.

Egyptian Hieroglyphs DNA Text 1. Embodied in Rosetta Stone Human brain 2. Known glyphs Greek and demotic Human language 3. Unknown glyphs Old Egyptian Cell language 4. Semantic connection Decree of Memphis Isomorphism between cell and human language 5. Key to decoding ‘Logophonogram’ Conformons

The contents of Table 8 reveal some unexpected connections:

1) The human brain may be analogous to the Rosetta Stone in carrying both the known text (written in human language) and the unknown text (written in cell language). Cell language is expressed in molecules such as DNA, proteins, and RNA, whereas human language is expressed most likely in terms of neuronal firing patterns encoding macroscopic symbols such as written letter, words, and sentences. 2) DNA embodies cell language just as hieroglyphs embodied the Old Egyptian language. 3) Just as the Decree of Memphis provided the common referent for both the known and unknown texts inscribed on the Rosetta Stone, so the isomorphism between cell and human languages recently uncovered [1] may provide a common framework connecting the known and the unknown texts written within human language (see the fourth row in Table 8). Cell and human languages are isomorphic in the sense that they both obey a common set of semiotic principles [1-5], including rule-governed creativity, double articulation, maximum information principle (due to arbitrariness of signs), discreteness of signs, and semanticity (see the bottom of Figure 18). 4) Conformons may play a key role in decoding DNA, just as the concept of ‘logophonograms’ played an essential role in deciphering Egyptian hieroglyphs (see the fifth row in Table 8). S. Ji. 50

5) The fact that the human brain carries both cell and human languages that are isomorphic may have profound implications in linguistics, cognitive sciences, philosophy, art, and religion. S. Ji. 51

Part III Cells

6 The Cell as the Atom of Semiosis

The following statement is often made, serving as a useful metaphor:

The cell is the atom of life. (48)

In addition, it is here suggested that

The cell is the atom of semiosis. (49)

The term ‘semiosis’ is defined as any physicochemical processes that are mediated by signs such as communication, computation, and DNA-directed construction, which was referred to as the C-triad in [199]. One consequence of combining Statements (48) and (49) is the corollary that the cell provides the physical basis and mechanisms for both living processes and semiois. A theoretical model of the cell, capable of achieving both these functions, were first proposed in 1983 in an international conference on the Living State held in Bhopal, India and hence was named the Bhopalator [56, 57]. One of the basic principles underlying the Bhopalator is that of information-energy complementarity as manifested in two ways – as conformons (conformational strains of biopolymers harboring mechanical energy in sequence-specific sites; see Section 5.1) and IDSs (intracellular dissipative structures; see Section 6.2). After reviewing the salient features of the Bhopalator, these two key concepts underlying the cell model will be discussed in detail.

6.1 The Bhopalator

It has been known since the mid-19th century that the cell is the smallest unit of the structure and function of all living systems, and yet it was not until 1983 that the first comprehensive theoretical model of the cell was proposed [56, 57]. In that year, I addressed both the energetic and informational aspects of life in a theoretical model of the living cell called the Bhopalator. The name Bhopalator reflects the fact that the cell model was born as a result of my participation in the Seminar on the Living State in Bhopal, India. The suffix, “-ator” indicates that the model is based on the assumption that the cell is a self-organizing chemical reaction-diffusion system (i.e., dissipative structures). The Bhopalator model of the cell consists of a set of arrows and nodes enclosed within a 3-dimensional volume delimited by the cell membrane (Figure 19 below). The system is open so that it can exchange matter and energy with its environment (see Arrows 19 and 20 in Figure 19 below). The arrows are directed, indicating the direction of flows of information driven by free energy dissipation. The solid arrows indicate the flow of information from DNA to the final form of gene expression postulated to be the S. Ji. 52 dissipative structures investigated by Prigogine and his coworkers [49, 50] (see Section 3.14). These dissipative structures are in turn assumed to exert feedback controls over all the solid arrows, as indicated by the dotted arrows. One of the most distinct features of the Bhopalator is probably the role assigned to dissipative structures (of which there are three basic classes to be explained in Section 6.7). These are assumed to be both the final form of gene expression and the immediate causes for cell functions (see Section 3.14). This notion may be schematically depicted as shown below, omitting the feedback interactions for simplicity:

DNA ---> RNA ---> Proteins ---> Dissipative Structures ---> Functions (50) 1 2 3 4 5

In other words, the Bhopalator model of the cell maintains that, without dissipative structures, it is impossible to couple genetic information stored in DNA (see #1 above) to the functions of the cell, namely, its phenotype (see #5). Thus, the dissipative structure concept popularized by Prigogine is of critically importance in understanding the molecular mechanisms underlying cell functions, which is consistent with Prigogine’s definition of the living cell expressed in Statement (30).

Figure 19. The Bhopalator – a molecular model of the living cell proposed at The International Seminar on Living State ~ II held in Bhopal, India, in 1983 [56, 57]. S. Ji. 53

As pointed out above, the Bhopalator model of the cell contains two features that are novel to biology– intracellular dissipative structures (IDSs) and conformons. Although there were no direct empirical evidence for these concepts when the cell model was first proposed in 1983, the experimental data supporting IDSs and conformons emerged in 1985 and the early 1990’s, respectively, as reviewed in the next two subsections.

6.2 IDSs (Intracellular Dissipative Structures)

A direct experimental support for the concept of IDSs was reported by Sawyer et al in 1985 [95], who measured the intracellular calcium ion gradients formed inside the human neutrophils as a prelude to their migration toward particles (see Figure 20).

Figure 20. Intracellular Ca++ ion gradients generated in the cytosol of a migrating human neutrophil. The intracellular Ca++ ion concentration was visualized using the Ca++- sensitive fluorescent dye, Quin2. The pictures in the first column are bright- field images, and those in the second column are fluorescent images showing S. Ji. 54

intracellular calcium ion distributions (white = high calcium; gray = low calcium). The pictures in the third column represent the color-coded ratio images of the same cell as in the second column. Images on the first row = Unstimulated neutrophil. Images on the second row = The neutrophil migrating toward an opsonized particles, ‘opsonized’ meaning “being treated with certain proteins that enhance engulfing” by neutrophils. Images on the third row = The neutrophil with pseudopods surrounding an opsonized particle. Images on the fourth row = The neutrophil after having ingested several opsonized particles. Before migrating toward the opsonized particle (indicated by the arrows in Panels D & G), the intracellular Ca++ ion concentration in the cytosol was about 100 nM (see Panel C), which increased to several hundred nM toward the advancing edge of the cell (see Panel F).

6.3 Complementary DNA Arrays and New Cell Biology

The advent of the microarray technique in molecular biology in the mid-1990’s [96-102] marks an important turning point in the history of cell biology, comparable to the discovery of DNA double helix in 1953. Although there remain many unsolved problems, both methodological [103] and biological [104-106], this novel technology possesses a great potential to make fundamental contributions to advancing our basic knowledge about the workings of the living cell, with important consequences in medicine, biotechnology, and pharmaceutical industry. It is ironic that the development of the microarray technique, which was critically dependent on the molecular biology of DNA, ushered in an era of a paradigm shift, away from DNA towards a system-based biology that emphasizes the workings of the living cell as an organized system of biopolymers in contrast to the earlier emphasis placed on the workings of individual biopolymers (mainly DNA, RNA & proteins). In other words, DNA not only opened the era of molecular biology in the mid-20th century but may have also signaled its end in the last decade of the same century, by having given birth to the microarray technique (or more accurately cDNA array techniques, since cDNA fragments can be fabricated into either microarrays or macroarrays [107]) and the emergence of the systems biology. The genome-wide expression data revealed by the cDNA array technique can no longer be rationally accounted for solely on the basis of the principles and knowledge gained from molecular biology of individual biopolymers [35, 105, 106]. We need new approaches and perspectives that are deep and rich enough to adequately cope with the ever increasing avalanche of cDNA array data on the Internet. A microrarry refers to a microscopic slide (or its equivalent), about 2cm by 2 cm in dimension, divided into, typically, 10,000 squares, each of which containing a fragment of DNA (complementary DNA, or oligonucleotides) that is complementary to a stretch of the genome encoding a mRNA molecule [100]. Therefore, using one microarray, it is possible now to measure simultaneously the levels of 10,000 mRNA molecules or more in a biological sample. Before the development of the microarray technique, it was possible to study only one or a few gene expression products at a time. It is unfortunate that, from the beginning of the microarray era, leaders in the field have created the impression that the microarray technique allows biologists to measure S. Ji. 55 rates of gene expressions (to be denoted as TR, transcription rates [107]) by measuring mRNA levels (to be denoted as TL, transcript levels [107]). In other words, they have created the scientific atmosphere in which it is deemed legitimate to accept a simple one- to-one correspondence between TL and TR. The following quotations reflect such a lax attitude in microarray field (emphasis is mine):

“ ... Microarrays prepared by high-speed robotic printing (51) of complementary DNAs on glass were used for quantitative expression measurements of the corresponding genes. . . . “ [97].

“Oligonucleotide arrays can provide a broad picture of the (52) state of the cell, by monitoring the expression level of thousands of genes at the same time. . . “ [101].

“. . .. DNA microarrays, permits the simultaneous (53) monitoring of thousands of genes. . . “ [102].

These statements would be correct if the term “genes”(in bolds) were replaced by “mRNA levels” or “transcripts”. In other words, workers in this field routinely conflate “genes” with ‘transcripts’ and consequently TR with TL, leading to numerous false positive and false negative conclusions in interpreting microarray data. Most investigators in the field seem to think that there is no harm in using the terms “gene expression” and “mRNA levels” interchangeably, but the investigations by Garcia- Martinez et al [107] and by others [200] have now clearly demonstrated that the mixing of these two terms can lead to erroneous conclusions [104, 105, 106]. Because of the experimental difficulties involved in measuring TR, it was not until 2004 that J. Perez-Ortin and his colleagues in Valencia, Spain, succeeded in measuring both the TR and TL values simultaneously of the whole genome of budding yeast subjected to glucose-galactose shift [108-110]. It is well known that when budding yeast cells are derived of glucose they undergo a profound metabolic transition from fermentation (converting glucose to ethanol) to respiration (converting ethanol to carbon dioxide and water) known as the diauxic shift [110]. When these TR values are plotted against TL values, highly nonlinear trajectories were obtained as shown in Figure 21. Previously to these data, people routinely assumed that TR would be a simple linear function of TL, but as can be seen in Figure 21, TR is clearly not linearly related to TL in about half of the time. (The components of the TL-TR trajectories that are parallel to a straight line with a slope of about 1 indicate linear correlations between TL and TR.)

S. Ji. 56

A fTL-fTR Plot 1 B fTL-fTR Plot 3 1.2 1.2 1 1 1 1 1 0.8 0.8 0.6 TR 0.6 0.4 0.4 6

0.2 6 0.2 FoldChanges in

0 FoldChanges in TR 0 0 0.5 1 1.5 0 2 4 6 Fold Changes in TL Fold Changes in TL

C fTL-fTR Plot 10 D fTL-fTR 19 1.2 2 1 1 6 1.5 0.8 1

0.6 6 TR 1 0.4 0.5

0.2 FoldChanges in

FoldChanges in TR 0 0 0 1 2 3 0 0.5 1 1.5 Fold changes in TL Fold Changes in TL

Figure 21. Plots of fold changes in transcription rates (TR) and transcript levels (TL) of budding yeast during metabolic transitions caused by glucose-galactose shift. These four examples (for mRNA molecules encoded in genes #1, #3, #10, and #19) were chosen randomly out of the 5,184 mRNA molecules investigated by Perez-Ortin and his coworkers [107]. Fold change in TL, denoted by fTL, is defined as the ratio of TL at time t over the TL at t = 0, i.e., fTL = TL/TL0.

Each plot shows the results of 6 measurements at t = 0, 5, 120, 360, 450, and 850 minutes after glucose was replaced with galactose in the growth medium. Experimental evidence indicates that TL is determined by the balance of two opposing processes– the transcription of genes into RNA or mRNA (i.e., TR) and the degradation of mRNA into shorter fragments (whose rate is denoted as TD, transcript degradation rates) (as predictable from the Principle of Dynamic Balance between Production and Degradation described in Section 3.14), so that the following relation holds:

d(aTL)/dt = b(TR) – c(TD) (54) where a, b and c are the parameters whose magnitude may or may not depend on individual mRNA molecules. If we assume that a and c are constant for the yeast genome and b is a function of individual mRNA molecules (reflecting the peculiarities of the experimental method for measuring TR, known as the nuclear run-on technique [107]), then Eq. (54) can be converted into

d(fTL)/dt = A(fTR) – B(fTD) (55) S. Ji. 57 where A = b/a and B = c/a and “fX” indicates “fold changes in X” as defined in the legend to Figure 21. Integrating Eq. (55) leads to

fTL = ∫[A(fTR) – B(fTD)]dt (55a)

We can draw two important conclusions from Eq. (55a): 1) Since there are three variables in Eq. (55), it is impossible to determine any one of them without also measuring one of the remaining two. For example, it would be impossible to determine A(fTR) by measuring fTL alone (because of the B(fTD) term, contrary to what has been routinely assumed in the field of microarray data analysis, and 2) Since there are at least three possibilities for the direction of changes in d(fTL)/dt in Eq. (55) - increase (+), no change (0), or decrease (-) – and, for each one of which, there are again three possible mechanisms for the term [A(fTR) – B(fTD)] to be (+), (0) or (-) (see Table 1 in [35], there are nine possible mechanisms for regulating d(fTL)/dt and hence the TL values [35, 105]. Each of the 9 possible mechanisms inferred above is associated with a unique RNA turnover mechanism involving a system of enzymes (e.g., RNA polymerase, ribonucleases, other regulatory factors), and hence it is logical to refer to it as an RNA turnover module or simply RNA modules [35, 105]. It should be pointed out that RNA modules invoked here are examples of IDSs (i.e., intracellular dissipative structures; see Section 3.14), since they are not permanent equilibrium structures such as RNA polymerases and electron transfer complexes but are transient ones that are called into action by appropriate signals when needed and dissolve into their components when their biological function is accomplished, very similar to what Norris et al referred to as “hyperstructures” [111]. Related concepts are also discussed by Srere (‘metabolons’ [112]), Hartwell et al (‘modules’ [113]), and Lehn (‘supramolecular chemistry’ [114]). The rich information contained in the TR and TL data measured by Garcia- Martinez et al [107] can be more accurately displayed in a 3-dimensional space consisting of the TR, TD and TL axes (Figure 22). The TD data were calculated from fTL and fTR data using Equation (55). For this purpose, the dfTL/dt at any time point was computed by differentiating the approximate TL function derived from TL data by an nth-degree polynomial fitting procedure, where n is the number of measuring time points, i.e., 6. One of the most striking features of the TR-TD-TL plots is that, despite major changes in the TR and TD values, the TL values often remain relatively constant. This may suggest that, during the metabolic perturbations caused by glucose-galactose shift, the yeast cell manages to maintain mRNA levels constant as long as possible, despite the fact that TR and TD undergo large changes. Alternatively, it may be concluded that, during the glucose-galactose shift, budding yeast cells regulate TR and TD is such a manner as to keep TL constant. We may refer to this interpretation as the “RNA homeostasis” or better, “RNA homeodynamic” hypothesis. (Homeodynamics is here defined as the maintenance of dynamic patterns of the changes in intracellular components, including steady-state patterns. Thus defined, homeodynamics includes homeostasis as one of its species). Similar phenomenon has been observed with respect to the intracellular levels of ATP under a wide variety of cell metabolic conditions [115]: S. Ji. 58 i.e., the intracellular ATP levels remain relatively constant despite great changes in the rates of ATP synthesis and utilization.

Figure 22. The 3-dimensional plots sowing the dependence of the mRNA levels (TL) on the rates of transcription (TR) (denoted as tr) and mRNA degradation (TD) (denoted as v3). The vertical lines indicate the TL values plotted on the z-axis. Each plot shows the identity of the gene encoding the mRNA under observation. These mRNA molecules (coded by genes 1, 5, 6 and 8) are arbitrarily selected out of about 6,000 mRNA molecules investigated in [107]. (I thank Drs. Sunil Dhar and Robert Miura, both of NJIT, for their help in preparing the plots shown in this figure.)

One of the universal features of the dynamics of TL in the TL-TR-TD space is the turning point occurring at around 120 minutes after the glucose-galactose shift. This is believed to be due to the switch in the metabolic patterns in budding yeast from fermentation to respiration. Therefore we can divide the trajectory of TL into two parts— one before and the other after the turning point. The trajectory before the turning point will be referred to as the F (fro fermentation) phase and that after the turning point as the R (from respiration) phase. The angle that the F and R phases make at the turning point (to be called the ‘FR angle’) can be used as a quantitative measure of the reversibility of the control mechanisms of RNA metabolism in budding yeast: The smaller the FR angle, the more reversible is the control mechanism of RNA metabolism (or the larger the FR angle, the more irreversible is the control mechanism). Evidently, the dynamics of the TL trajectory associated with gene 1 shows an almost zero FR angle, whereas that associated with gene 6 exhibits an FR angle close to 90 degrees. The reason for such differential behaviors exhibited by FR angles is not yet clear and requires further investigations. But one interesting possibility is that the ‘bioactivity coefficients’ (defined in Section 3.15) of mRNA molecules showing nonzero FR angles may undergo changes S. Ji. 59 at the turning point in such a manner as to make the effect of the TD on lowering TL is less that the effect of TR in increasing TL (to account for the fact that the R phase is always on the left-hand side of the F phase in Figure 22). If this interpretation turns out to be correct after further studies, the cDNA array data such as shown in Figure 22 may provide a firm empirical evidence for the validity of the Principle of Differential Activities.

6.4 An Analogy between Atomic Physics and Cell Biology

When I saw a picture similar to the one shown in Figure 23 in a seminar on microarray data analysis a few years ago, I was struck by the superficial similarity between this picture and atomic absorption spectra such as shown in Figure 24. Figure 23 is about the locations and abundances of genes and related structures along the chromosomes of the unicellular organism, C. neoformans [116]. In contrast Figure 24 shows the wave numbers (i.e., the number of waves per cm) of light absorbed when the electron in the hydrogen atom undergoes transitions from one energy level to another [60, 117]. Figure 23 is about the cell and Figure 24 is about the atom, but they both reflect the probabilities of some events occurring along appropriate structural coordinates in each system.

Figure 23. The C. neoformans genome with each chromosome represented as a colored bar. Genomic features are pseudocolored, from red (high density) to deep blue (low density). These include the density of genes, transposons, expressed sequence tags (ESTs), and predicted single nucleotide polymorphisms (SNPs) [116].

It may be useful to imagine what may be referred to as the “gene expression activity spectrum (GEAS)” which consists of the addresses or locations of all the genes along chromosomes indicated on the x-axis (as in Figure 23) and the corresponding rates of gene expression (i.e., TR) along the y-axis. For the human genome, the GEAS may look similar to Figure 24, only with a larger set of lines, approximately 1000 per chromosome, with varying heights reflecting different rates of corresponding transcription. S. Ji. 60

Figure 24. The atomic spectra of the hydrogen atom. (1) The hydrogen atom absorption lines detected in the light from Zeta Tauri. (2) The same absorption lines observed in the light from another star, 11 Camelopaadlis [60, p. 472].

Figure 25. Energy levels of the hydrogen atom. [60, p. 475].

S. Ji. 61

If this qualitative comparison is valid, cell biologists might learn some useful lessons from the history of atomic physics. For example, in 1885, Lyman and others discovered that the absorption or the emission lines of the hydrogen atom obeyed a simple formula,

2 2 ν = R (1/n2 - 1/n1 ) (56) where ν is the wave number of the light, R is the Rydberg constant (109677.581 cm-1), and n2 and n1 are positive integers associated with the excited and the ground states, respectively, of the electron in the hydrogen atom [60, 117] (see Figure 25). This formula remained a mystery until 1913, when Niels Bohr proposed a theoretical model of the hydrogen atom based on experimental data obtained by Rutherford and the theoretical concept of the quantum of action invoked by M. Planck in 1900. The Bohr’s atomic model led to the correct interpretations of the meanings of n2 and n1 in Equation (56) and to the calculation of the Rydberg constant from fundamental constants of physics. The atomic absorption spectroscopy discussed above suggests an interesting analogy: cDNA array technology may be to the cell biology of the 21st century what the line spectroscopy was to the atomic physics of the 20th century. This and other related comparisons are summarized in Table 9. This table is not meant to be exhaustively complete but lists only those items related to the theoretical cell biological research that I have done during the past 30 years and thus may omit many important contributions made by other researchers, for example, the work of Craig Benham on SIDSs (stress- induced duplex destabilizations) which are directly related to the concept of conformons [77, 78, 195]. The term ’ribonoscopy’ appearing in the third row and the third column is here defined as the experimental technique that allows biologists to study genome-wide (i.e., over the whole set of genes in a cell) changes in the levels of the RNA (ribonucleic acid) molecules inside the cell measured by cDNA arrays and other methods as functions of environmental perturbations. So defined, ribonoscopy may be viewed as the experimental technique for doing “ribonomics”, a term recently coined by Keene meaning the genome-wide study of RNA changes in cells [119]. In other words, it may be said that ribonoscopy is to ribonomics what atomic spectroscopy is to atomic electronics. Ribonomics is further explained in the next section. “Ribons” appearing in the fifth row and the third column is defined as the genome-wide spatial and temporal patterns of mRNA levels or concentrations inside the cell (such as exemplified by the RNA trajectories shown in Figures 21 and 22). Since the mRNA levels are determined by both the transcription rate (TR) and degradation rate (TD) (see Equation (54)), ribons are evidently species of IDSs (intracellular dissipative structures; see Section 6.2). The advantage and the utility of the term ribons derive from the fact that it is directly connected to the rich results of the theories of dissipative structures worked out by Prigogine and others in the 1980’s [49, 50].

S. Ji. 62

Table 9. An analogy between atomic physics and cell biology based on the similarity between line spectroscopy in atomic physics and cDNA array technology in cell biology.

Parameter Atomic Physics Cell Biology Time 19th-20th Century 20th-21st Century Experimental Atomic cDNA Array Technology Technique Absorption/Emission (‘ribonoscopy’ ?)(1995) [96-102] Spectroscopy (19th C) Experimental Atomic Line Spectra mRNA Levels in the Cell Data Regularities Lyman Series RNA Metabolic Modules (ribons) (?) Balmer Series Genetic networks (?) Ritz-Paschen Series Cell Metabolic Networks (?) Brackett Series Pfund Series Theoretical Bohr’s Atom (1913) The Bhopalator (1985) [56, 57] Model Basic Concepts Quantum of Action (1900) The Conformon (1972) [32-34] IDSs (1985) [46] Cell Language Theory (1977) [1-3] Theory Quantum Theory (1925) The Conformon Theory of Molecular Machines (1974) Cell Language Theory (1997) Molecular Information theory (2004) [35] Philosophy Complementarity (1915) Complementarism (1993) [11, 30] A Unified Theory of Physics, A Theory of Everything Biology, and (e.g., the Tarragonator (2005) [118]) Philosophy

6.5 Ribonomics

The cDNA technology has opened a whole new field of study in mRNA metabolism, recently termed “ribonomics” [119]. One of the objectives of ribonomics would be to understand, for example, why individual mRNA molecules exhibit their temporal trajectories as they do in the TR-TD-TL space (see Figure 22). This would be akin to atomic spectroscopists trying to understand the shape of atomic spectra in terms of the internal electronic structure of the atom. I would predict that the RNA trajectories S. Ji. 63 such as those shown in Figure 22 will eventually allow us to determine the dynamic structures of the metabolic and genetic regulatory networks underlying the behaviors of living cells. For convenience, the temporal trajectories of RNA levels visualized in the TR-TD-TL space will be referred to as “ribons” (as already discussed in Section 6.4). Furthermore, ribons are identifiable with IDSs (intracellular dissipative structures), since they are composed of “ribonucleic acid” concentration gradients (in space and in time) inside the cell. In a similar vein, the dissipative structures composed of “active” DNA (e.g., euchromatin) concentration gradients may be logically referred to as “deoxyribons”(which may in turn be divided into ‘genons’, modules of interacting genes and ‘regulons’, modules of interacting regulatory DNA sequences), and those composed of protein concentration gradients as “proteons”. Evidently, the free energy needed to maintain all these dissipative structures is provided by exergonic chemical reactions which they themselves catalyze, either directly (in the case of proteons) or indirectly (in the case of ribons and deoxyribons). In another sense, these dynamic structures can be viewed as genes, mRNA, and proteins in action made possible by the combination of traditional structures with free energy mostly derived from ATP. There may well be other similar dynamic structures that contribute to IDSs, the immediate cause for (or identical with) all cell functions or behaviors:

IDSs = Genons + Ribons + Proteons + other dissipative structures (57)

6.6 Dssipative Structures as the Third Articulation in Cell Biology

The theoretical framework of the contemporary molecular biology admits of only two kinds of structures -- covalent and noncovalent. According to the Bhopalator model of the living cell [56, 57], however, there exists a third class of structures known as 'dissipative structures' or 'intracellular dissipative structures (IDSs)', that plays an essential role in energy and information transductions going on in the living cell (see Figure 19). These three classes of structures are briefly characterized below. 1) Covalent structures (also known as configurations in chemistry) = Systems of atoms that are linked by covalent bonds, i.e., through sharing one or more pairs of valence electrons. Examples: polypeptides formed from amino acids linked through peptide bonds; genes constructed from nucleotides linked through phosphodiester bonds. 2) Noncovalent structures (also called conformations) = Molecules or molecular complexes that involve noncovalent bonds such as van der Waals forces, hydrophobic bonds, hydrogen bonds, and electrostatic bonds. Examples: protein folds; DNA folds; hormone-receptor complexes; transcription initiation complexes (containing 50 or more polypeptides); 'metabolons' [112], 'modules' [113]; 'hyperstructures' [111]. 3) Dissipative structures = Concentration or mechanical stress gradients [49, 50]. Also called 'intracellular dissipative structures' or IDSs [56, 57]. Dissipative structures result from nonequilibrium distributions (i.e., gradients) of molecules or conformational energy (or forces) in space or in time.

S. Ji. 64

Conformational energy localized within sequence-specific domains of biopolymers are known as conformons [32, 34]. Covalent structures are too well known in chemistry to need any explanation. Noncovalent structures are less well known. They have been carefully investigated in physical organic chemistry and in the field of protein folding during the past several decades. Jean-Marie Lehn aptly refers to the study of noncovalent structures as the "supramolecular chemistry", in contrast to "molecular chemistry", which Lehn defined as the study of covalent structures [114]. The third class of structures, which were referred to as "intracellular dissipative structures" or IDSs in 1985 [56], is least well known among biologists, although they deal with them all the time, in the form of various ion gradients across cell membranes, action potentials, mRNA levels measured with cDNA arrays (see Figure 22), and mechanical stress gradients underlying chromosomal dynamics accompanying gene expression, cell motility, and cell shapes. The concept of dissipative structures as dynamic structures requiring dissipation of free energy for their maintenance was firmly established in physical chemistry, both empirically and theoretically, by the 1960's and 70's [49, 50]. The characteristics of these three classes of structures and their postulated roles in cell biology are summarized in Table 10:

Table 10. Three classes of molecular structures underlying cell functions.

Structure Covalent Noncovalent Dissipative 1. Alternative Configurations Conformations Hyperstructures [111] Name Modules [113] 2. Bond Strength 50~100 1~5 ~0 (Kcal/mole) 3. Degree of Strong Weak None Constraints 4. Structure Individual Individual Group properties properties properties 5. Role in Cell Second First Third Language articulation articulation articulation 6. Human Language Words Sentences Texts Analogue 7. Linguistic/Semiotic Representation Judgment Logical processes Function Denotation Reasoning Computing

Row 2 = It requires 50 to 100 Kcal/mole of energy to break covalent bonds between, say, C and C, or C and H [60, p. 59]. Noncovalent bonds are relatively weak requiring S. Ji. 65 only 1 to 5 Kcal/mole to break. Gradients of chemicals do not depend on interaction energies between the entities whose concentration varies in space or in time. Row 3 = Covalent bonds provide strong constraints on the motions (vibrational, or rotational) of atoms within a molecule. Noncovalent bonds constrain much less the motions of atoms in a molecule or the motions of molecules bound to one or more of other molecules. Macroscopic concentration gradients provide little or no microscopic constrains to the motions of diffusible molecular entities. Row 4 = Covalent and noncovalent structures are the properties of individual molecules, whereas gradients are the properties of a group or a collection of molecules. Row 5 = First (i.e., formation of sentences from words) and second (formation of words from letters) articulations are well known in linguistics, which was imported into molecular and cell biology in 1997 [1]. It was suggested that covalent structure formation is analogous to second articulation and the noncovalent structure formation to first articulation. Although it seems logical to extend the concept of articulation from the first and the second to a third (formation of texts from ordered arrangements of sentences, i.e., Sentences --> Text), I have not yet seen such a possibility discussed in any linguistics literature. In any case, I have suggested in Table 10 that the third articulation so defined is analogous to dissipative structures in cell biology. Row 6 = See Row 5. Row 7 = The semiotic role of covalent structures is suggested to be the representation of some structures or processes other than themselves. For example, a set of genes forming parts of DNA can be viewed as representing a phenotype visible to human eye and hence is not the same as the original set of genes. Noncovalent structures, e.g., a folded protein, is capable of making a judgment (or logical or rational selection) in the sense that, of the thousands or even millions of different folds of an enzyme that are in thermal equilibria, only one or a few such conformations appear to be selected by evolution to perform catalysis [33]. This situation can be described as the active conformation of the enzyme 'deciding' to lower the activation energy of a target chemical reaction or 'selecting' the same reaction. A dissipative structure established inside the cell (e.g., the potassium electrochemical gradient across the cell membrane) results from the combined actions of a set of enzymes participating in associated metabolic pathways. In the case of the potassium electrochemical gradient across the cell membrane, the enzymes implicated would include those of the glycolytic pathway, the Krebs cycle, and oxidative phosphorylation, in addition to the Na+/K+ ATPase that pumps ions across the cell membrane driven by ATP hydrolysis. The activities of these enzymes (which must number at least a couple of dozens) must be well coordinated so as to maintain the electrochemical gradients of potassium and sodium ions under prevailing environmental condition. Such coordinated metabolic activities of dozens of enzymes is suggested here to be akin to the human reasoning process, involving the execution of a set of transformations of signs (symbolic) based on logical rules. Evidently third articulation is operative in both human and cell languages. In other words, these two languages seem isomorphic with respect to the third articulation, as they are shown to be with respect to first and second articulations [1].

I will employ the term “triple articulations” to indicate the three kinds of articulations possible in linguistics as shown in Table 10: first articulation = formation S. Ji. 66 of sentences from words; second articulation = formation of words from letters; third articulation = formation of texts from sentences. Analogous articulations in cell biology are suggested to be: first articulation = noncovalent structures; second articulation = covalent structures; third articulation = 'dissipative structures', such as ion gradients and mechanical stress gradients in the cytoskeletons of the cell. One advantage of discussing these articulations in linguistics and cell biology together is to extend their functions well established in one field to the other (in the spirit of the table theory discussed in Section 3.8). If the extension of the meaning of the third articulation in linguistics to cell biology is valid, one astounding result arises logically -- the cell can reason through dissipative structure, just as humans can reason using texts (one of the simplest examples being the categorical syllogism containing three sentences, a major and a minor premise and a conclusion), referred to by Peirce as 'argument' [16, pp. 40-42]). This conclusion was included in the last row of Table 10. If what I suggest in this table is valid, we can logically conclude that the cell can reason (i.e., carry out logical processes, or molecular computation). In fact, we may state that the living cell is the smallest material system that can reason (i.e., think, or compute) [57, 104, 120]. Not only, the cell may be viewed as the smallest material system that can construct or modify itself following molecular instructions encoded in DNA [57], thus accounting for morphogenesis in principle. A theoretical model of the cell called the Bhopalator (see Figure 19) is based on these triple articulations [56, 57, 121]. One deduction to be made based on this model of the cell is that, in order to understand the phenomenon of morphogenesis, for example, it may be necessary for biologists to view the living cell as a reasoning being (the reasoning processes dependent on dissipative structures, as well as on covalent and noncovalent ones), just as the human brain or the computer reasons based on words, sentences, texts/programs. If such molecular reasoning is performed by the cell, ribons (i.e., dissipative structures comprising mRNA molecules in the cell; see above) can be suggested to play a fundamental role as a molecular texts in action, in agreement with the postulate of Wang and Gribskov who view RNA as the primary memory of the cell and DNA as the secondary memory [122]. Thus it is possible to view human, computer, and cellular reasoning processes as physical or physicochemical processes taking place in nature obeying a set of common rules [51], and it is here hypothesized that these rules can be identified with the regularities of the sign processes proceeding in the Universe, as glimpsed by C. Pierce about a century ago [12 -17, 66].

6.7 Three Classes of Dissipative Structures – with Fixed, Moving, and Informed Boundaries

Though Statements (30) and (31), reflecting Prigogine’s conception of organisms, are correct in the sense that organisms are indeed dissipative structures, these statements are incomplete in the sense that not all dissipative structures (e.g., the chemical concentration waves exhibited by the Belousov-Zhabotinsky reaction in Figure 10) are organisms. I agree with H. Pattee who stated in [44] that

“a productive approach to the theories of life, evolution, and (59) S. Ji. 67

cognition must focus on the complementary contributions of non-selective law-based material self-organization and natural selection-based symbolic organization (meaning the genetic mechanisms; my addition)”.

According to this so-called matter-symbol complementarity view, dissipative structures alone, as exemplified by the Belousov-Zhabotinsky (BZ) reaction, is not sufficient to give rise to life, because they are devoid of any symbolic elements that encode evolutionary history. There is a great similarity between Pattee’s emphasis on a symbolic aspect of organisms and my emphasis on the essential role of information in living systems in Statement (32). One way to overcome this criticism may be to invoke a new class of dissipative structures not evident in purely chemical-reaction diffusion systems such as the BZ reaction but clearly evident in enzyme-catalyzed chemical reaction-diffusion systems (e.g., see Figure 20) where the physical law-governed chemical reaction-diffusion processes and evolutionary rule-governed boundary conditions (e.g., amino acid sequences of enzymes) interact to accomplish biologically meaningful processes (e.g., chemotaxis). Thus, I have been led to entertain the notion that there are three distinct classes of dissipative structures depending on the physicochemical nature of the boundaries delimiting such structures. These are compared in Table 11.

Table 11. Three classes of dissipative structures: (i) Dissipative structures with fixed boundaries, (ii) dissipative structures with moving boundaries, and (iii) dissipative structures with informed boundaries.

Dissipative Structures with Fixed Moving ‘Informed’ Boundaries Boundaries Boundaries (DSwFB) (DSwMB) (DSwIB) Boundaries Walls of Surface of the object Catalytic residues of reaction vessels moving in fluid enzymes at their active sites that carry genetic information (see Figure 4 in [33, 122] Examples Belousov- Turbulent fluid flow Enzymes utilizing Zhabotinsky patterns around binding energy to reaction moving objects catalyze chemical reaction (also called the Circe effect [62]) Theoretical Brusselator [49] Navier-Stokes Conformon model of Models equation [123] enzymic catalysis [31, 32, 35] S. Ji. 68

The main difference between moving boundaries and informed boundaries is that the latter not only moves but also “communicates” with the chemical reactions that they catalyze through exchanging energies with both the chemical reactions and thermal environment as explained in Section 5.2. If the classification scheme in Table 11 is valid, we can identify all molecular machines and motors in action driven by chemical reactions as “dissipative structures with informed boundaries” (DSwIB). Because of the informed nature of the molecular structures of enzymes, enzymes can search out their target molecules to bind or target reactions to catalyze and execute motions in the direction of achieving informed/ instructed functions. When a right set of such informed molecular machines are put in a confined space such as the interior of the cell, the molecular machines can find their correct targets to interact with, forming a molecular machine network which executes a collective non-random molecular motions which we recognize as life. Such a view of life seems consistent with the conclusion drawn in Section 10.6 that life is a highly condensed form of information, just as matter is a highly condensed form of energy. Therefore, we are entitled to view the living cell as a “super-dissipative structure with informed boundaries” or “super-DSwIB”. I suggest that the super-DSwIB is capable of any computation, communication, and construction, the microscopic analogue of the Turing machine and von Neumann’s Universal constructor [70] combined.

6.8 The Triadic Structures of the Living Cell

Dissipative structures are distinct from covalent and conformational (also called noncovalent) structures in that they are 'far-reaching' or 'global' in contrast to covalent and noncovalent structures whose effects are localized within one (in the case of covalent structures) or a set of contiguous molecules in physical contact (in the case of noncovalent structures). The 'far-reaching' effects of dissipative structures inside the cell can be mediated by electric field (in the case of action potentials) or mechanical tensions (in the case of the cytoskeletons based on a network of interconnected microfilaments, intermediate filaments and microtubules, all driven by energies resulting from ATP or GTP hydrolysis). Ingber and his colleagues have obtained direct experimental evidence showing that local perturbations of a living cell under mechanical tensions can be propagated throughout the cell, which phenomenon they referred to as 'tensegrity', or tensional integrity [124]. Thus, Ingber’s tensegrity can be viewed as an example of intracellular dissipative structures. It is suggested here that dissipative structures are essential (along with covalent and noncovalent ones) for cell reasoning and computing because their 'far-reaching' effects provide mechanisms to coordinate many physicochemical processes occurring individual locations inside the cell, just as the 'far-reaching' axons allow the physicochemical processes occurring within individual neurons to get coordinated and organized in the brain to effect human reasoning.

Table 13. Three categories of structures in the cell and the brain. The third structure, which is built on the first two structures, is thought to be essential for S. Ji. 69 reasoning/computing, or the ability of a physical system to respond to input stimuli according to a set of rules or programs.

Peircean Categories Level Firstness Secondness Thirdness

Cell Chemical Reactions Biopolymer-Biopolymer Gradient structures Interactions (space- and time- dependent)

Brain Gradient Structures Information Transmission Neural Networks (e.g., membrane (from one neuron to (connected via action potentials) another) potentials and neuro- transmitters; space- and time-dependent)

If these arguments are valid, the following conclusions may be drawn: 1) A new category of structures (called dissipative structures) must be invoked before biologists can understand the workings of the living cell (e.g., mitosis, morphogenesis, signal transduction, etc.), just as physicists had to invoke the notion of strong force (in addition to electromagnetic and weak forces) before they could explain the stability of atomic nuclei. . 2) Reasoning process is not unique to the human brain but can be manifested by cellular and abiotic systems meeting certain structural requirements. The postulated ability of the cell to reason seems consistent with the isomorphism thesis between cell and human languages [1-5], since, without being 'rational', no humans nor cells can be expected to be able to use a language. 3) Humans can reason (i.e., the Thirdness phenomenon exists in the human brain), only because cells and abiotic systems in nature in general behave rationally (and not chaotically); i.e., the Thirdness phenomenon exists in Nature, independent of human mind. The universality of Thirdness asserted here may be closely related to what Rosen called Natural Law that guarantees the ability of human mind to model nature [125].

6.9 A Topological Model of the Living Cell

There is now an abundance of experimental evidence to support the conclusion that cells, both normal and diseased, are affected by 5 distinct classes of factors or determinants (see Table 14): To graphically represent the equal importance (to be referred to as the ‘equipotency hypothesis’) of all of these 5 factors in determining the properties and behaviors of the cell, I recommend using the body-centered tetrahedron as shown below:

S. Ji. 70

Biochemicals (B) | | | | | Environment (Env) _ / \ _ / \ Proteins (P) / \ / \ RNA (R) DNA (D)

Figure 26. A topological model of the living cell viewed as a body-centered tetrahedron (BCT). The tetrahedron is the simplex of the 3-dimensional space, an n-dimensional simplex being defined as the simplest polyhedron in an n dimensional space [126, p. 146, Vol. III]. The six edges connecting the four vertices (B, D, R and P) are not explicitly shown. One unique feature of BCT is that all the nodes are in simultaneous contact with one another, a topological property suggestive of the physical situation where affecting one node affects all the others.

Table 14. The five classes of factors affecting the behavior of living cells

Determinants Examples Explanations 1. DNA Mutations in certain genes Mutated genes lead to alterations in (e.g., p53 gene [201]) lead to protein amino acid sequences which cancer and other pathological often lead to altered protein changes. conformations and functions. 2. RNA Colon cancer cells show RNA molecules not only mediate Statistically significantly (through mRNA) but also regulate different patterns of changes in (through snRNA, and microRNA) the mRNA levels compared to those coupling between genotypes of normal cells [202] (DNA) and phenotypes (proteins). 3. Proteins A diarylquinoline drug, known Proteins are the only macromolecules as R207910, binds to the in the cell that can harvest free energy membrane component of the from chemical reactions they ATP synthase in catalyze. This means that, without Mycobacterium tuberculosis, proteins, no energy-requiring thereby killing the organism processes (without which no life can [203]. exist) can be carried out by the cell. Proteins are molecular engines/motors/machines S. Ji. 71

out of which the cell is constructed. 4. Biochemicals Depriving oxygen kills all Without biochemicals, no chemical aerobic cells. reactions would occur inside the cell, depriving the cell of all free energy sources and hence ultimately of life. 5. Environment Most cells can survive only Cells have evolved to survive and within narrow ranges of perform their specialized functions environmental conditions to only under stringently defined which they have adapted environmental conditions. For through long evolutionary example, although all the cells in history, including temperature, the human body have about 30,000 pressure, genes, different subsets of them are humidity, neighboring cells, expressed in different parts of our availability of light and nutrient body, depending on their local chemicals, etc. environmental conditions, leading to the liver, the kidneys, the heart, or the brain, etc.

Several testable predictions in the field of microarrays [100] may be formulated based on the BCT model of the cell: 1. When the level of a mRNA molecule changes in a cell due to some perturbations, it is impossible to attribute such changes solely to DNA changes (e.g., changes in transcription rates or TR; see Section 6.3), because proteins (e.g., transcription factors, RNA polymerase, RNA binding proteins, histones, DNA topoisomerases, etc.), biochemicals (e.g., ions, pH, ATP, etc.), and environmental conditions (e.g., tissue specificity, microcirculatory situations, neighboring cells, etc.) may be responsible for at least a part of the changes in mRNA levels being measured (cf. the’equipotency hypothesis’ above). 2) We may distinguish two kinds of causalities -- the direct and the indirect. For example, if a perturbation causes mRNA levels to change, it may be due to the direct effects of DNA, proteins, biochemicals, or environmental factors which are affected by the perturbation, or due to an indirect effects of DNA, proteins, or biochemicals which affect mRNA levels through their direct actions on the environment. 3) Mutations in DNA may affect mRNA levels measured with microarrays in some but not all mutated cells, depending on the environmental conditions (e.g., tissue specificity, or microcirculatory variations within a given tissue).

Finally, it should be pointed out that the body-centered tetrahedron was first used to depict the models of the human body [38, p. 144] and the Universe [38, pp. 233-237], and later was employed to model computation [51], the human mind (see Section 8.5 below), and a theory of everything (see Section 10.7). The Bhopalator, the first S. Ji. 72

theoretical model of the living cell [38, p. 80] (see Figure 19) also can be represented using BCT by making the assignments shown in Table 15).

Table 15. A mapping of the Bhopalator model of the living cell (Figure 19) onto the topology of the body-centered tetrahedron (Figure 26).

The Nodes and Edges The Nodes and Arrows of of the Body-Centered the Bhopalator model of Tetrahedron the Cell 1. Env Arrows 19 & 20 2. Biochemical Substrates & IDSs (Intracellular Dissipative Structures) 3. Proteins Enzymes 4. RNA RNA 5. DNA DNA 6. B-P Arrows 5, 6, 7, 8, 9 & 19 7. P-R Arrows 12 & 13 8. P-D Arrows 10 & 11

As evident above, the topology of BCT appears to have the property of being applicable to a wide range of phenomena in the Universe. The triadicity of the Peircean metaphysics and semiotics may also be viewed as an aspect of BCT (see Section 10.7 & Table 36). The universality of BCT may indicate that the number 5 inherent in BCT may have a fundamental significance in the 3-dimesnional space in which we live. S. Ji. 73

Part IV The Mind

7. Semiotics and Life Sciences

Semiotics and the science of life (i.e., biology, agriculture, and medicine) have had a long and venerable history of interactions. For example, ancient physicians in both East and West diagnosed the diseases of patients based on symptoms; farmers used cloud patterns to predict weather, etc. But the connection between semiotics and life sciences in general may have undergone a significant weakening when the reductionistic scientific methodologies were imported into life sciences from physics and chemistry around the 19th century. The reductionistic trend in physics began with the birth of the mathematically oriented physics following the successful experiments with falling bodies performed by Galileo in the 17th century [204]. After over three centuries of domination of physical and biological sciences by reductionism, a new trend seems to be emerging in physics and life sciences that emphasizes integration and holism, without necessarily denying the fundamental importance of reductionism [205]. As an example of such a new trend, we may cite the recent recognition that there exists an unexpected similarity (or isomorphism) between the molecular language used by the living cell, the atom of life, and the symbolic languages used by Homo sapiens [1-5]. One of the major objectives of this contribution is to reveal the deep connection between life and semiosis, thereby laying a firm foundation for a semiotic theory of life, a theory of living systems viewed as systems of molecular signs and sign processes (see Section 10.7). Any theory purporting to explain the phenomenon of life must also account for nonlife, since these are inseparably linked. In fact, according to complementarism [11, 30], life and nonlife are but complementary aspects of reality, reminiscent of the wave/particle duality of light in physics. Therefore, the topics discussed in this contribution are uncommonly diverse and eclectic, reflecting the limitless complexities of life as well as nonlife. However, it is hoped that, through the application of semiotic principles, we can formulate a comprehensive and coherent theoretical framework that will maximally capture the regularities hidden under the complexities of both life and nonlife.

7.1 The Biology-Linguistics Connection

The idea that language may provide a useful metaphor or analogy for biology was seriously entertained by Pattee [71] and Marcus [127] already over three decades ago. The biology-linguistics connection was further strengthened by the recognition of the isomorphism between cell and human languages discussed in Sections 3.7. Another indirect evidence for this connection came to light recently from a somewhat unexpected direction. During the DIMACS (Discrete Mathematics and Computer Science) Workshop on Bimolecular Networks: Topological Properties and Evolution, held at Rutgers on May 11-13, 2005, Alfonso Valencia from the National S. Ji. 74

Center of Biotechnology in Spain delivered a thought-provoking lecture entitled “Biodegradation network, and all what we need for its study”. Based on his extensive experience in data mining in the field of the protein structure-function correlation, he expressed his pessimism about predicting protein folds and functions from amino acid sequence data. Dr. Valencia’s pessimism seems to go against the prevailing presupposition of biophysicists specializing in protein folding that 3-dimensional folds of proteins should be ultimately predictable based on their sequence information. Dr. Valencia’s “pessimistic” conclusions regarding protein structure-function correlation reminded me of a similar situation that transpired in the field of the theory of algebraic equations between the 16th and the mid-19th century [126, pp. 261-278, Vol. I]. The following is a list of the key developments in the history of this branch of mathematics: 1) Ferrari (1522-1565) solved the general fourth-degree polynomial equation of the type, x4 + ax3 + bx2 + cx + d = 0 in a radical form. 2) In 1824, Abel (1802-1829) proved that the fifth-degree polynomial equations could not solved in radical forms. 3) In a paper entitled “Memoir on the conditions of solvability of equations in radicals” published in 1846, Galois (1811-1832) provided an explanation for why the fifth-degree polynomial equations could not be solved in radicals. In the process, Galois was led to invent the group theory.

The analogy that I see between algebra and protein molecular biology may be summarized as shown in Table 16.

Table 16. The postulated analogy between the unsolvability of the 5th-degree polynomial equations and the unpredictability of the 3-dimensional structures of proteins based on their amino acid sequence data.

Theory of Algebraic Theory of Protein Folds/Functions Equations Solved What Is 4th-Degree polynomial 2-Dimensional structures equations By Whom Ancient mathematicians Computational biologists of the 20th Century Unsolvable What Is 5th-degree polynomial 3-Dimensional structures based equations on amino acid sequence information Proved By N. H. Abel in 1824 A. Valencia and others in 2005 (?) Why E. Galois in 1846 Living systems are rule-governed Unsolvable creative systems (21st Century) (see Explained By Section 3.16) New Theory Group Theory A Theory of Everything S. Ji. 75

Emerging (e.g., the Tarragonator; see Section 10.7)

One possible explanation for why the protein structures and functions cannot be predicted based on their amino acid sequence data alone may be because biology is a complementary union of the predictable (the domain of physics) and the unpredictable or the creative (the domain of linguistics; cf. the rule-governed creativity [1, 128]). This idea is summarized in Table 17, which also introduces the concept of matter- symbol complementarity advanced by H. Pattee over the past three decades [40, 45]. This concept was referred to as the von Neumann-Pattee principle of matter-sign complementarity in 1999 [3], to reflect not only the history of the development of this important concept but also its affinity to the more general notion of information/energy complementarity embodied in the new biology-based philosophical framework known as complementarism [11, 30]: Matter-symbol complementarity [40, 45] and matter-sign complementarity [3] may be viewed as special cases of the more general information- energy complementarity.

Table 17. A postulated relation among physics, biology, and linguistics.

Relative Contributions of Physics Biology Linguistics (Law-governed) (Matter/Symbol (Rule-governed) Complementary) Laws + + + + + + + + + (governing Matter/Energy) (100% ?) (50%?) (0 % ?) Rules (encoded in Symbols, carriers + + + + + + + + + of Information) (0%?) (50%?) (100%?)

If the content of Table 17 is correct, biology may be described as neither physics nor linguistics but both. In other words:

“Biology is a complementary union of physics and linguistics.” (60)

“Biology has two complementary aspects--physics and linguistics.” (61)

“Physics and linguistics are complementary aspects of biology.” (62)

Since linguistics is an important branch of the more general theory of signs, namely semiotics, it appears logical to inquire into the possible connection between biology and semiotics.

S. Ji. 76

7.2 Semiotics: The Peircean Theory of Signs

Although the study of signs can be traced back to the beginning of the human history as already indicated, the investigation of signs as a fundamental science did not begin until the Portuguese monk John Poinsot (1589-1644) and the American chemist- logician-philosopher C. S. Peirce (1839-1914) (apparently independently of Poinsot) undertook their comprehensive and systematic studies of signs [37].

7.3 Peircean Definition of Signs

Peirce made a major contribution to philosophy by constructing his triadic theory of signs. Peirce defined a sign as follows: "A sign, . . . , is something which stands to somebody for something in some respect or capacity." (see Statements (1) and (1a)). As pointed out by Peirce [140], we think in signs. If you have any doubt about this statement, just think of an elephant. Do you have an elephant in your head ? Of course not. Since you don't have any elephant in your head, whatever you had in your head (most likely a series of neuronal firing patterns proceeding somewhere within your cerebral cortex) when you thought about an elephant must be a sign for the elephant. Peirce recognized three major classes of signs – 1) Iconic signs (e.g., portraits, diagrams, tables), 2) Indexical signs (e.g., smoke as a sign of fire, a finger Figure 27. Charles Sanders pointing to an apple), and Peirce (1839-1914) 3) Symbolic signs (e.g., words, sentences, texts). This division of signs is based on Peirce's realization that signs (also called sign vehicles or representamens; see Figure 1) exhibit three distinct relations with their object (or referents) -- iconic, indexical, and symbolic. This fact is often interpreted by many as there being three "kinds" of signs, namely, "iconic signs", "indexical signs", and "symbolic signs", but the term "kinds" of signs can be misleading if one takes them to mean separate and independent entities. A more accurate statement is that "there are three aspects to a sign", so that a sign can exhibit one or more of these three aspects, depending on the context involved. I cite below three authors, including Peirce himself, whose quotations support such an interpretation.: 1) C. S. Peirce cited in [129]: : (Each of the above three aspects) ". . .serves to bring before the mind objects of a different kind than those revealed by the other species of signs. . . . the most perfect of signs are those in which the iconic, indicative [or indexical], and symbolic characters are blended as equally as possible".

S. Ji. 77

2) M. H. Fisch [130, 131]: ". . . there are no absolutely pure symbols, indexes, or icons, but that these are elements or aspects that vary greatly in their relative prominence from sign to sign."

3) V. M. Colapietro [129]: ". . .the relation of sign to object may be based on a possible resemblance, an actual reaction, or a habitual connection. Insofar as a sign is related to its object by virtue of a possible resemblance, it functions iconically; insofar as this relationship depends on an actual reaction or set of such reactions, it functions indexically; and insofar as this relationship results from a habitual connection it functions symbolically. The fact that a sign functions in one of these ways does not preclude it from functioning in one or both of the other ways; in fact, the most perfect signs function in all three of these ways.”

In other words, according to Peirce, a sign has an irreducibly triadic nature and hence cannot be reduced to any one or pair of its iconic, indexical and symbolic functions. It may be that this irreducibly triadic nature of signs ultimately results from the irreducibly triadic nature of reality exhibiting the aspects of Firstness, Secondness, and Thirdenss. For convenience, we may refer to this doctrine of sign as "the icon/index/symbol triadicity" of signs and represent it diagrammatically as follows:

Iconic

Representamen-Object Relation of a Sign =

Indexical Symbolic

Figure 28. The icon/index/symbol triadicity of a sign. The essence of this figure is that a Peircean sign exhibits iconic, indexical and symbolic functions simultaneously (symbolized by the brackets), although the degree of prominence of each aspect varies from sign to sign. Please note the similarity between this figure and Figure 1, probably because both these are specific realization of the general principle of the irreducibility of triadic reality diagrammatically depicted in Figure 6.

Three corollaries may be drawn from the icon/index/symbol triadicity of Peircean signs: 1) Even linguistic marks such as English words and sentences, possess, in addition to symbolic functions, their iconic and indexical functions. 2) Molecular signs such as hormones, RNA and DNA segments possess symbolic functions in addition to their iconic (e.g., structural complementarity between S. Ji. 78

hormones and their receptors) and indexical functions (e.g, free energy of interaction between DNA to DNA-binding proteins during gene expression). 3) Elementary particles of physics may be regarded as Peircean signs with iconic and indexical functions predominating and their symbolic function suppressed, waiting to be reified or instantiated when a right set of environmental conditions present themselves.

7.4 Peircean Signs as Gnergons

Peirce explains how signs can be divided into a total of nine classes [16]: "Signs are divisible by three trichotomies; first, according to (63a) as the sign itself is a mere quality ('qualisign'; my addition)), is an actual existent ('sinsign'), or is a general law ('legisign'); secondly, according as the relation of the sign to its object consists in the sign's having some character in itself ('icon'), or in some existential relation to the object ('index'), or in its relation to an interpretant ('symbol'); thirdly, according as its Interpretant represents it as a sign of possibility ('rheme') or as a sign of fact ('dicent sign') or a sign of reason ('argument')."

The above classification of signs by Peirce is summarized in Table 18.

Table 18. The classification of signs according to the ontological/material trichotomy (first row) and the phenomenological/formal trichotomy (first column) [16].

Firstness Secondness Thirdness (Potentiality) (Facts, Actuality) (Law, Habits)

Firstness (Sign) Qualisign Sinsign Legisign

Secondness (Object) Icon Index Symbol

Thirdness (Interpretan t) Rheme Dicent Sign Reason (or Dicisign) (or Argument)

As can be seen, there are a total of nine types of signs in Table 18. I suggest the following ideas for the possible connection between Peircean signs and Bohrian complementarity (as generalized in 1995 [11]; see Section 3.3): 1) Each of the nine types of signs appearing in Table 18 has a dual aspect (reminiscent of the wave/particle duality of light) -- the ontological (or material) and the phenomenological (or formal) aspects (when realized). S. Ji. 79

2) The ontological/material aspect of a sign can be identified with energy/matter properties, while the phenomenological/formal aspect with informational properties. 3) Therefore, Peircean signs given in Table 18 can be viewed as examples of what I called "gnergons" in 1991, defined as discrete units of gnergy, the complementary union of energy ('ergons') and information ('gnons') that is postulated to be the ultimate cause of, or ground for, all self-organizing (or pattern-forming) processes in the Universe [11]. 4) Since all sign processes (semiosis) can be viewed as species of self-organizing processes, ultimately driven by the free energy of exergonic chemical reactions (e.g., ATP hydrolysis or oxidation of NADH) or physical processes (e.g., solar radiation, the Big Bang, etc.), it would follow that gnergons are the ultimate causes (or drivers) of semiosis [11] (see Appendix IX). 5) According to complementarism [11], a scientific metaphysics rooted in both contemporary biology and Bohr's philosophy of complementarity, the ultimate reality is a complementary union of information and energy, i.e., gnergy. Since signs are species of gnergons (see Appendix IX), it may be concluded that Peirce's semiotics falls within the domain of the application of complementarism. This admittedly audacious claim may be supported by the following arguments:

(a) Peirce's semiotics deals mainly with macroscopic signs, i.e., signs with macroscopic dimensions "perfusing" the Universe. The birth of molecular biology was about four decades away when Peirce died in 1914 [36]. (b) Complementarism can be applied not only to Peirce's semiotics (as suggested above) but also to molecular and cell biology, as evident in the formulation of theory of "microsemiotics" based on the gnergy concept [4, 5]. Microsemiotics can be regarded as synonymous with the twin theories of the living cell known as biocybernetics and cell language theory [5]. It is also possible to refer to the combination of biocybernetics and cell language theory as ‘molecular information theory’ [35]. In other words, ‘microsemiotics’ and ‘molecular information theory’ are synonymous. (c) Thus the following relations are suggested:

Complementarism = Macrosemiotics + Microsemiotics (63) = Peirce's semiotics + Biocybernetics/Cell Language = Peirce’s semiotics + Molecular Information Theory Consistent with Peirce's triadic ontology, the principle of complementarity itself may be manifested in the Universe in three distinct modes: Firstness = Complementarity in metaphysics (e.g., Yin and Yang as complementary aspects of the Tao of Lao-tze [193]; Extension and Thought as complementary aspect of Substance of Spinoza [28, 189]; Body and Mind as complementary aspects of Flesh of Merleau-Ponty [29]) Secondness = Complementarity in physics (e.g., the wave/particle duality of light) Thirdness = Complementarity in psychology (e.g., hysterical anesthesia of William James [206]), physiology (i.e., the left/right hemispheric S. Ji. 80

specialization [207]), and molecular and cell biology (e.g., the information/energy complementarity of gnergy [11])

These ideas may be schematically represented as follows:

FIRSTNESS (Complementarity in Metaphysics)

SECONDNESS THIRDNESS (Complementarity in Physics) (Complementarity in Life Sciences )

Figure 29. The three modes of being of the principle of complementarity also called "general complementarity" [11]). Life sciences as Thirdness, may serve as the mediator between metaphysics and physics.

If the ideas expressed in Figure 29 are correct, the divergence of physics and metaphysics that has been going on since Galileo’s experiments with falling bodies in the 17th century may be reversed through the mediating role of the life sciences of the 21st century. In other words, the principle of information/energy complementarity manifest in biology [11] may provide the theoretical framework to integrate metaphysics (science of information?) and physics (science of energy).

7.5 The Quark Model of Peircean Signs

There is a striking family resemblance between particle physics and Peircean semiotics. There are at least 11 instances in physics where the number 3 plays an essential role as in Peircean semiotics, as pointed out by Christiansen [132]: (1) 3 generations of elementary particles -- quarks (2nd row in Table 19) and leptons (3rd row). The electric charges of the elementary particles are given on the left-hand side of the first column, which applies to the second and the third columns as well. The mass of each quark is given in the square brackets in the unit of 106 electron Volts. Table 19. 3 generations of elementary particles. Generation 1 Generation 2 Generation 3 +2/3e: up (u) [5] charm (c) [350] top (t) [>80] -1/3e: down (d) [9] strange (s) [160] bottom (b) [4800] -1e: electron muon tauon 0: electron neutrino muon neutrino tauon neutrino The mass of the elementary particles belonging to each generation increases in the order 1 < 2 < 3, which reflects the order of discovery: The S. Ji. 81

accelerators measuring light particles were developed before those able to detect heavier ones. (2) Heavy nuclear particles, or baryons, consist of 3 quarks. (3) Quarks have 3 colors -- red, green, and blue. (4) Quarks have strange electric charges, each being 1/3 of the electron's charge, e. (5) Particles have 3 internal properties -- spin, mass, and charge. (6) Particles undergo 3 kinds of interactions -- strong, electroweak, and gravitational. (7) Strong interactions are mediated by 23 = 8 gluons. (8) Quarks and leptons interact weakly mediated by 3 intermediate vector-bosons. (9) Space has 3 dimensions. (10) There are 3 types of units - length, mass, and time. (11) There are 3 fundamental constants of nature – the Planck constant (h), the speed of light (c), and the gravitational constant (G).

In addition, the number three appears in the following contexts: 1) The smallest unit of all networks or mathematical categories comprises three elements – the initial and final nodes and the arrow connecting them. 2) The probability theory of Kolmogorov comprises three and only three axioms; (1) 0< p< 1, (2) p + p’ = 1, where p’ is the complement of p, and (3) p(A+B) = p(A) + p(B). 3) Most of chemistry and biology can be accounted for in terms of the interactions among 3 particles -- photons, electrons, and protons. 4) There are only three major classes of polymers in living systems – nucleic acids, proteins, and carbohydrates. 5) The human brain is constructed out of the three main functional structures – the right and left hemispheres, and the corpus callosum [207].

These examples strongly support the notion that there is something fundamental about the number 3 in particle physics and semiotics as well as in some other fields, perhaps because all material entities and processes in the Universe (including the human brain) are derived from gnergy, defined as the complementary union of information and energy (see Sections 3.1 and 3.2) and the concept of complementarity itself being triadic. The purpose of this section is to ‘rationalize” the Peirce’s classification of signs based on the analogy between Peircean signs and the quark model in elementary particle physics.

7.5.1 9 Types of Signs Peirce defined 9 types of signs (as discussed in Section 7.4) and 10 classes of signs based on these 9 types [16]. For convenience, I will refer to the former as e-signs (“e” standing for “elementary”) and the latter as c-signs (“c” standing for “compound”). But it is not clear to me how he was able to derive the 10 classes of what I here call c-signs from the 9 types of e-signs. Sheriff provides a clear rationale for deriving 9 types S. Ji. 82 of signs in [16]. Before quoting Sheriff, the following preliminary information is provided to facilitate understanding what Sheriff has to say: 1) According to the metaphysics of Peirce, there are three and only three modes of being in the Universe, Firstness, Secondness, and Thirdness [12, pp. 75-76]. 2) The term "sign" is used in two ways -- as a triadic entity, i.e., as "representamen- object-interpretant", or as a monadic one, i.e., as "representamen". Representamen is also called "sign vehicle", and "interpretant" is the effect a sign has on the interpreter (see Figure 1). 3) Peircean "trichotomies" (i.e., the action or results of cutting something into three) refer to the following three sets of three terms (see Table 18): i) qualisign, sinsign, legisign ii) icon, index, symbol iii) rheme, dicent sign, argument. The following quote from Sheriff [16] succinctly describes how Peirce arrived at his 9 types of signs (see Table 18): "We have already defined Firstness, Secondness, and Thirdness as ontological modes of being (possibility, fact, and law) and as experienced in consciousness (feeling, reaction-sensation, and general conception). When Peirce analyzed his definition of a sign (as representamen-object-interpretant) in relation to each of these categories, he concluded that a sign or representamen is one of three kinds (Qualisign, Sinsign, or Legisign); it relates to its object in one of three ways (as Icon, Index, or Symbol); and it has an interpretant that represents the sign as a sign of possibility, fact, or reason, i.e., as Rheme, Dicent Sign, or Argument. These three sets of three terms are the 'trichotomies' in Peirce's semiotic. The strange words in this paragraph have evoked much confusion and disgust and have been obstacles to the influence of Peirce's thought. But if we keep the following in mind, these terms become quickly understandable: the first term in each trichotomy describes the Firstness of the sign, object, and interpretant; the second term in each trichotomy describes the Secondness of the sign, object, and interpretant; and the third term in each trichotomy describes the Thirdness of a sign, object, and interpretant.”

The content of the above paragraph is summarized in Table 20, which shows the formal (see the left-most column) and ontological (the upper-most row) characters of e- sings. This table also proposes a new system of notation of e-signs.

Table 20. The 9 types of signs (e-signs). Each of the 9 signs can be characterized in terms of the notation, Si,j, where the first running index i refers to the formal category (i.e., the rows), and the second running index j refers to the ontological category of S, an elementary sign (i.e., the columns). Both i and j run from 1 to 3. For example, e-S1,3 denotes Legisign, and e-S2,1 refers to Icon, etc. The traditional names of the e- signs are given above the systematic names.

S. Ji. 83

Secondness (2) Thirdness (3) Firstness (1) (Actuality, (Law, Habit, (Quality, Possibility) Reaction) Representation)

Firstness (1) Qualisign Sinsign Legisign (Representamen) (S1,1) (S1,2) (S1,3)

Secondness (2) Icon Index Symbol (Object) (S2,1) (S2,2) (S2,3)

Thirdness (3) Rheme Dicent sign Argument (Interpretant) (S3,1) (S3,2) (S3,3)

7.5.2 Ten Classes of Signs According to Peirce, an embodied sign is composed of 3 elementary signs. That is, c-Sign = 3 e-Signs (64) Unlike baryons which are unordered sets of 3 quarks, c-signs are ordered sets of three e-signs:

c-Sign = {(S3,j), (S2,j), (S1,j)} (65) The 10 classes of signs defined by Peirce in the form of Eq. (65) are listed in Table 21. Table 21. The 10 classes of c-signs. The new code for each class is given below the traditional designation.

Class Compound Sign (c-Sign) Example

Rheme-icon-qualisign First Feeling of red (S3,1)-(S2,1)-(S1,1)

Rheme-icon-sinsign Second An individual diagram (S3,1)-(S2,1)-(S1, 2)

Rheme-index-sinsign Third A spontaneous cry (S3,1)-(S2, 2)-(S1, 2)

Dicent sign-index-sinsign Fourth Pointer position of a meter (S3, 2)-(S2 , 2)-(S1, 2)

Fifth Rheme-icon-legisign Circuit diagram S. Ji. 84

(S3,1)-(S2,1)-(S1, 3) Computer icon

Rheme-index-legisign Sixth A demonstrative pronoun (S3,1)-(S2, 2)-(S1, 3)

Dicent sign-index-legisign Seventh A street vendor's cry (S3, 2)-(S2, 2)-(S1, 3)

Rheme-symbol-legisign Eighth A common noun (S3,1)-(S2,3)-(S1,3)

Dicent sign-symbol- Ninth legisign A proposition (S3, 2)-(S2, 3)-(S1, 3)

Argument-symbol-legisign Tenth Inference (abduction, induction, deduction) (S3, 3)-(S2, 3)-(S1, 3)

Two features of the 10 c-signs given in Table 21 are noteworthy: 1) The epistemological categories (i.e., the i values) of the e-signs in a c-sign decreases as 3, 2, and 1 in conformity with Eq. (65). 2) The ontological categories (i.e., the j values) of the 3 e-signs constituting a c-sign obey the following rules [208] to be referred to as the “Peirce’s rule of embodied signs’ or just “Peirce’s rule” for simplicity:

"The j value of (S1,j) cannot be lower than the j value of (S2,j), (66) which in turn cannot be lower than the j value of (S3,j)."

Statement (66) will be referred to as the Peirce’s rules of embodied signs.

7.5.3 The Derivation of the 10 Classes of Signs from 9 Types of Signs Based on the Analogy between e-Signs and Quarks in Elementary Particle Physics.

To the best of my knowledge, Peirce did not provide any justification as to why three (and not some other numbers of) e-signs constitute a c-sign. This gap may be filled by the postulated ‘isomorphism’ between quarks and e-signs as explained in Table 22.

Table 22. The family resemblance (to be called the "isomorphism" loosely) between elementary particles and Peirce’s sign types.

Parameters Particle Physics Semiotics

9 e-signs (or sign types): Elementary 6 quarks (u, d, c, s, t, b) (S ), (S ), (S ), (S ), (S ), (S ), (S ), Units 6 leptons 1,1 1,2 1,3 2,1 2,2 2,3 3,1 (S3,2), (S3,3) (see Table 18) S. Ji. 85

Compound 10 classes of c-signs (or embodied signs) ~ 60 baryons Units (see Table 21)

Syntactic Rules 3 quarks in a baryon 3 e-signs in a c-sign

mass (5 - 5,000 MeV) 1) The epistemic categories of e-signs are Order electric charge (+2/3, denoted by their first subindexes 1, 2, & 3. Parameters -1/3) 2) The ontological categories of e-signs are color charge (r, g, b) denoted by their second subindexes 1,2, & 3.

1) The epistemic categories of the 3 e-signs constituting a c-sign must increase from right to left ("the right-to-left parity") Semantic 3 quarks in a baryon 2) The ontological categories of the 3 e-signs Constraints must color white. constituting a c-sign must obey “Peirce’s rule” given in (66) above.

If the "isomorphism" between elementary particles and Peircean signs is real as claimed in Table 22, we can make the following predictions: 1) Just as baryons form atoms in interaction with electrons, so may c-signs form higher-order signs in interaction with the electron analogs of semiotics, which may well turn out to be the sign processor, leading to a tetrahedron (the usual triangle with an extra node above it; see Figure 40) as a geometric representation. This sign tetrahedron (in contrast to the traditional sign triad) may be called the "atomic signs." 2) Atoms form molecules through covalent (or strong) bonds. Similarly, "atomic signs" may form "molecular signs" through “strong bonds". 3) Just as molecules form molecular complexes (e.g., enzyme-substrate complexes) through weak noncovalent bonds, so "molecular signs" may interact through weak bonds to form "sign complexes". The "semions" proposed recently by R. R. Gudwin [209] may be viewed as an example of "sign complexes" defined here. 4) Inside the living cell, molecular complexes interact selectively forming dynamic networks of molecular complexes. Likewise, "sign complexes" may interact selectively in space and time to form dynamic "sign networks" realizing or executing some complex tasks, similar to the "semionic networks" of Gudwin [209].

7.5.4 An Application of the Concept of c-Signs to Molecular Biology: Microsemiotics

Having defined the 10 classes of c-signs, let us now apply them to the specific case of DNA as an example of a molecular sign. S. Ji. 86

i) The word "DNA" must have three formal elements -- representamen, object, and interpretant -- and each of these in turn have three values depending on its ontological status. a) Representamen -- DNA as a representamen can be either one of the three possibilities, i.e., qualisign, sinsign, and legisign. If we are thinking about general principle of DNA as the carrier of genetic information, it would be a "legisign". On the other hand, if we are considering a specific DNA molecule isolated from, say, Drosophila melanogaster, with a specific set of genes encoded in it, then DNA would be a "sinsign". If, through meditation, say, one can feel one's DNA molecules wiggling around in his/her brain cells either replicating or transcribing genes, DNA to such a person is a qualisign, or DNA in action in living cells may be another example of a qualisign from the point of view of the cell. So, DNA can be any one of these three representamens. b) Object -- The object of DNA as representamen could be any one of the tree possibilities -- icon, index, and symbol. The object of "DNA" would be indexical, if DNA acts as a sinsign (i.e., as the carrier of specific genes); symbolic, if DNA acts as a legisign (i.e., as a material substrate selected by biological evolution as a medium of encoding genetic information); and iconic, if DNA acts as qualisign (i.e., representing the way the cells must "feel" as when they divide to become a mouse, a horse, or a rose). c) Interpretant --- DNA interpretant can be any of the three possibilities -- rheme, dicent sign, and argument. DNA would be interpreted as representing a rhyme, if DNA acted as an iconic quailing (see Class 1 sign in Table21), an iconic sensing (Class 2), an indexical sensing (Class 3), an iconic elegizing (Class 5), or an indexical elegizing (Class 6), or symbolic elegizing (Class 8); it would be a decent sign, if DNA acted as indexical sensing (Class 4), or indexical elegizing (Class 7), or a symbolic elegizing (Class 9); and it would be an argument if DNA acted as a symbolic elegizing (Class 10). Thus, we can conclude that the word "DNA" can act as any one of the 10 classes of signs defined in Table 21, depending on the role it plays in a given context of discourse. The 10 classes of signs that Peirce "discovered" about 100 years ago based primarily on observations made at the level of human sign processes, which are by and large macroscopic in scale, appears to be applicable to sign processes in and among living cells which are microscopic in scale [4,5].

7.6 Semiosis: Real vs. Virtual

It is important to realize that semiosis (e.g., cloud formation before rain as perceived by farmers as signs of rain; appearance of bodily symptoms in sick persons as perceived by physicians) as a physical process long preceded the emergence of semiotics as a systematic study of sign processes carried out by humans. In other words, clouds preceded rain and symptoms appeared in diseased human body long before they were recognized as signs by appropriately trained human mind. This simple observation leads to the counter-intuitive conclusion that signs and sign processes can exist without human mind (although they would not yet have been given the labels ‘signs’ or ‘sign processes’). Deely refers to those signs that exist before human mind recognize them as such as S. Ji. 87

“virtual signs” and their processes as “virtual semiosis” in contrast to “actual signs” and “actual semiosis” that are recognized by human mind [133, 134]. This situation may be diagrammatically represented as in Figure 30:

Human Mind Virtual Signs/Virtual Semiosis Actual Signs/Actual Semiosis

Figure 30. The role of human mind in semiosis.

Sign processes, both virtual and actual, as defined in Figure 30, can be divided into distinct classes on the basis of the physical agent that carries out sign processing, as shown in Figure 31. Also, semiosis can be divided into macro- and microsemiosis based on the physical size of the signs being processed (Figure 32).

Anthroposemiosis (4)

Zoösemiosis (5) Biosemiosis (2) Phytosemiosis (6)

Semiosis Cytosemiosis (7) (1) (also called Molecular Semiosis, Microsemiosis [5])

Physiosemiosis [37, 133, 134] (3)

Figure 31. Division of semiosis into 7 groups or branches according to the nature of sign processors.

Macrosemiosis (8)

Semiosis (1)

Microsemiosis (9)

Figure 32. Division of semiosis into two branches based on the physical size of sign processors.

The contents of Figures 31 and 32 are summarized Table 23. S. Ji. 88

Table 23. The division of sign processes based on the nature and the physical size of sign processors.

Sign Process Sign Processor Size of Sign Processors 1. Semiosis the Universe Macroscopic 2. Physiosemiosis Non-living Systems Macroscopic 3. Biosemiosis Living Systems Macroscopic or Microscopic 4. Anthroposemiosis Homo sapiens Macroscopic 5. Phytosemiosis Plants Macroscopic 6. Zoösemiosis Animals Macroscopic 7. Cytosemiosis Cells Microscopic 8. Macrosemiosis Macroscopic Systems Macroscopic 9. Microsemiosis Microscopic Systems Microscopic

As alluded to earlier, in-depth and systematic investigations into the nature and function of signs, from portraits and weathercocks to words and ideas, were not undertaken until John Poinsot (1589-1644) and C. S. Peirce (1839-1914) began their systematic investigations in modern times [37, 133, 134]. The influence of Peirce’s theoretical works in semiotics is so overwhelming that many contemporary semioticians seem to regard semiotics as synonymous with the theory of signs formulated and developed by Peirce. I think such a view is unjustified and short-sighted. Since semiosis is a universal process that must have been going on since the beginning of human history (by one account even from before the Big Bang [38, pp. 154-163]) and will continue to do so far into the cosmological future, no single individuals, including Peirce and Poinsot, can be expected to develop a complete theory of semiosis in their lifetimes. To support this conclusion, we can cite the formulation of microsemiotics in 1997 [2, 3, 4, 5]. Microsemiotics (i.e., the study of sign processes mediated by molecules as carriers of information) was born as a logical consequence of the development of molecular biology which might be said to have begun in 1953 with Watson and Crick’s discovery of the DNA double helix and subsequent breaking of the genetic code. Peirce missed the molecular biological revolution by about 4 decades, so his semiotics could not address microsemiosis and hence was limited to studying macrosemiosis, particularly anthroposemiosis and physiosemiosis [1, 37, 133, 134]. The amount of the posthumous publications of Peirce’s works on signs are enormous and their interpretations are controversial. Modern biosemioticians are faced with the challenge of discerning to what extent, if any, the numerous theories, concepts, and conclusions that were formulated by Peirce on the basis of his investigations on macrosemiosis can be applied to the study of microsemiosis opened up by molecular and S. Ji. 89 cell biological revolutions of the 20th and 21st centuries. An equally important challenge is for modern semioticians and philosophers to find out, to what extent, if any, the semiotic analysis of living processes, aided by modern molecular and cell biology, can contribute to solving some of the thorny philosophical and metaphysical problems plaguing the field of semiotics. The former challenge may be easier to deal with than the latter one, since the principles and regularities underlying microsemiosis may be more readily uncovered due to the relative simplicity of microsemiosic systems (e.g., cells and neurons) associated experimental data compared to macrosemiosic systems (e.g., the human brain).

7.7 The Origins of Biological Information and Life

Any theory attempting to account for life cannot avoid facing the fundamental question about how life originated in the first place. One of the most physically realistic models of the origin of biological information (and hence of life) that I know of was proposed by P. W. Anderson and his colleagues in the early 1980’s [135, 136] (Figure 33). The model was based on thermal cycling (i.e., the cyclical changes of the temperature of the earth due to its daily rotation around its own axis) of an RNA “soup” presumed to be present somewhere on the primordial earth surface some 3 billion years ago. The following quotation from [136] captures the essence of his model:

“. . . The autocatalytic mechanism which must be at the core of any prebiotic evolution scheme is the complementary conjugation of polymeric molecules, nominally RNA. It is assumed that the thermal cycle periodically breaks up the weak conjugation bonds between RNA polymers, and at a later stage allows them to reconjugate randomly. Once two polymers have simultaneously conjugated with the same ‘template’, matching adjoining sequences (see the RNA double strands located on the bottom of Figure 33; my addition), they are permitted with some probability to bond completely together, thus elongating the chain and reproducing a longer sequence of the ‘template’. This is the basic autocatalytic process, while the basic energy source is a constant supply of energy rich monomers (or short sequences of 2 or 3 monomers) which are added at each cycle and can be joined to the sequences already present by the conjugation-thermal cycling process. To achieve realism and a reasonably steady state, we must also postulate an error probability and a probability of chain death and/or breaking.”

S. Ji. 90

Figure 33. The proposed model of the origin of biological information (and life) based on the concept of frustrations imported from spin glass physics [135-137]. Due to the presence of frustrations, some polymer chains cannot self-conjugate, thereby exposing single stranded segments to environment to act as templates.

Anderson based his model of the origin of biological information (considered here as synonymous with Pattee’s “messages”) on the concept of “frustrations” imported from spin glass physics [135-137]. Frustrations are physical systems with three or more components, each being able to exist in at least two energy (or spin) states (conveniently designated as + and –, or up and down, with opposite signs attracting and identical ones repelling each other) but, no matter how their spins are arranged, there exists at least one pair of components whose spins are parallel to each other and hence of a non-minimal energy. Anderson and his colleagues represented the nucleotide sequence of an RNA molecule as a string of binary digits or spins, designating G as + +, C as - -, A as + -, and U as - + (which obviously obeys the Watson-Crick pairing rule). This allowed them to calculate the free energy (called “spin glass Hamiltonian”, a mathematical function mapping spin configuration to the total energy of the spin system) of RNA molecules described as linear strings of spins. Furthermore, they were able to define what is referred to as the “death function” D(S) as a nonlinearly decreasing function of the spin glass Hamiltonian [135, 136]:

D(S) = 1/{exp[-H(S) + ρN] + 1} (67) where H(S) is the spin glass Hamiltonian or the total energy of the spin system S, ρ is a proportionality constant, and N is the number of spins in the system (which is less than 10 in the case studied in Figure 34 below). Repeated applications of Eq. (67) to a collection of a short RNA sequences showed that certain sequences died out with time (see the 7- and 8-mers in Figure 34) whereas certain others (see the 11- and 12-mers) grew with repeated ‘thermal cycling’. S. Ji. 91

Figure 34. The temporal evolution of RNA fragments obeying the death function, Eq. (67).

Frustrations embedded in physical systems including primitive RNA molecules (as contrasted with formal systems) are associated with both sequence information and mechanical energy, but Anderson utilized only the sequence information in synthesizing complementary RNA fragments (see Figure 33) (thereby satisfying the symbolic aspect of Pattee’s principle of matter-symbol complementarity [41, 43]) but did not capitalize the mechanical (or conformational) energy available in frustrations embedded in RNA to drive the synthesis of polymers (thereby failing to meet the material/energetic part of the matter-symbol complementarity of von Neumann and Pattee [3]). Anderson had to assume that “energy-rich” monomers, i.e., nucleoside triphosphates (or nucleotides), were already available in the primordial RNA soup, but the presence of nucleoside triphosphates in the primordial soup may be very unlikely in view of its chemical instability, even if they were assumed to be formed by accidental coupling of 5 molecules belong to three different molecular species -- a base, a sugar, and inorganic phosphate. To overcome what I believed to be the deficiency of the Anderson model of the origin of biological information (“deficient” from the perspective of the matter-symbol complementarity), I modified his model by utilizing not only the sequence information (as he did) but also the conformational energy associated with frustrations (which he ignored). This is tantamount to assuming that the frustrations embedded in RNA molecules are conformons (sequence-specific conformational strains). The resulting S. Ji. 92 conformon-based model of the origin of biological information (see Figure 35) was named “the Princetonator” to indicate the facts (i) that it is an example of self-organizing chemical reaction-diffusion systems (as indicated by the suffix, ator), and (ii) that it is an extended version of the model of the origin of biological information developed by Anderson and his group at Princeton. The Princetonator contains the following key postulates [38, pp. 224-225]:

1) On the primordial surface of the earth about 3 billion years ago, there existed a pool (to be called the “primordial soup”) of at least two short biopolymers, A and B, most likely RNA molecules. 2) Due to thermal cycling (caused by the daily rotation of the earth or other cyclic motions on the earth such as tidal weaves), the components of the primordial soup underwent periodic binding (e.g., due to low temperature; see Steps 3, 6 and 8 in Figure 35) and de-binding (e.g., due to high temperature; see Step 10) processes. 3) During the low temperature phase, some biopolymers form a complete intramolecular binding (see B after Step 3) and some others form an incomplete intramolecular binding due to the presence of frustrations (see the bulge in A after Step 3) entrapping a part (E) of the total energy flux, (E1 – E2), through the primordial soup. The bulge (i.e., frustration) is located in sequence-specific sites and carries mechanical energy, thus qualifying them as conformons [34]. 4) The bulge acts as a template for binding a set of monomers (i.e., nucleosides consisting of a ribose ring covalently linked to a base (symbolized as a dark square connected to a bar) and inorganic phosphate ions (symbolized as a filled circle) (see Step 6). 5) The binding of the monomers and inorganic phosphate moieties to the bulge is postulated to trigger a conformational transition of the template causing covalent bond formation between nucleosides and adjacent inorganic phosphates to produce a string of nucleotides (see Step 8). 6) During the high temperature phase, the bound RNA fragments dissociate into monomers (see Step 10), producing unchanged B and A with a part of it reproduced, which has a finite probability of being elongated further through the repetition of the thermal cycling, eventually reproducing the original template completely.

S. Ji. 93

Figure 35. A conformon-based model of the origin of self-replicating molecular systems that is constructed on the basis of the assumption that frustrations embedded in RNA carry both sequence information and mechanical energy and hence are examples of conformons [34]. The key features of this model is that the thermal cycle of the earth’s surface produce conformons in primitive RNA templates which can drive the synthesis of RNA fragments that are complementary to a portion of the templates, the repetition of which leads to a complete replication of some RNA templates but not others. Conformons are equivalent to frustrations entrapped in sequence-specific loci in primordial biopolymers. Reproduced from [38].

Pattee pointed out a set of logical and physical constraints that must be met by any satisfactory theory of the origin of life and biological information [41]: 1) The primeval ecosystem language = The global set of geophysical and geochemical constraints of the primeval earth surface that were conducive to the spontaneous generation of self-replicating molecular systems or molecular switches (see below). S. Ji. 94

2) complex molecular interactions leading to a very simple result = Communication among molecules obeys simple rules relative to the complex mechanisms underlying their interactions: Communication is in some way a simplification of a complex dynamical interactions. 3) switches = Physical devices whose function it is to turn on or off some physical or formal processes driven by energy dissipation. Networks of switches, often referred to as “sequential switching machines” or “automata”, can duplicate many of the most complex biological processes including human thought itself. 4) open-ended evolvability = Not all self-replicating systems can also evolve. In order for self-replicating systems to evolve in an open-ended manner, special requirements additional to those of self-replication must be satisfied. 5) stability = Of the many possible self-replicating systems that could have evolved spontaneously in the primeval ecosystem, only those with stability, reliability and persistence must have survived. 6) the ‘von Neumann limit’ = there exists a critical limit to the complexity of the network of switches which must be exceeded in order to effectuate self-replication. Since such a limit was first recognized by von Neumann, it is here suggested that the indicated limit be referred to as the ‘von Neumann limit’.

All of these requirements appear to be satisfied by the combination of the original model of the origin of biological information proposed by Anderson [135, 136] and the Princetonator [38], as summarized in Table 24.

Table 24. The logical and physical requirements for the mechanism of the origin of molecular messages (as specified by H. Pattee) are met by the combination of the Anderson model of the origin of biological information and the Princetonator.

Pattee’s Constraints Satisfied by on Mechanisms of the Origin of Life [41] Anderson’s Model The Princetonator

1. Primeval Ecosystem The ‘RNA soup’ on the surface of the earth subjected to thermal cycling 2. Simple Rules Conformon production and utilization 3. Switches Frustrated regions of RNA harboring conformons (see Figure 35) S. Ji. 95

4. Open-ended Thermally accessible conformations Evolvability (called virtual conformons [38, p. 136]) of RNA fragments that can drive self- replication when reified to real conformons upon coupling to exergonic chemical reactions, obeying the generalized Franck-Condon principle [38, pp. 50-56]. Due to thermal motions implicated, there is a finite probability of errors occurring during the conformon- driven copolymerization process, thus leading to mutations and open-ended evolution [44]. 5. Stability It is possible that n catalytically active molecular species (CAMS) must be co- localized in a small spatial volume (to be called the catalytic site) to effectuate spontaneous copolymerizations (see the Franck-Condon state defined on p. 433 in [31] and Figure 1 in [33]). If the probability of such a co-localization is P and the average probability of individual CAMS being located in the catalytic site is p, the following relation holds: P = pn. This simple power law indicates that the stability and the probability of spontaneous formation of the self- replicating systems (SRS) increases and decreases, respectively, with increasing n. That is, the larger the value of n, the smaller is the probability P and the greater would be the stability of SRS against its accidental destruction by thermal motions. 6. von Neumann limit The von Neumann limit below which no SRSs can evolve may be identified with the exponent n in the relation, P = pn, because n is determined by the balance between two opposing processes, namely, the spontaneous generation of SRSs (whose probability decreases with n) and the stability of SRSs (whose probability increases with n). We may refer to n as S. Ji. 96

the von Neumann exponent for convenience of discussions.

7.8 The von Neumann Questions and the Conformon Theory

In a thoughtful article entitled "The Physics of Symbols: Bridging the Epistemic Cut," published in a special issue of BioSystems [42] honoring his life-long contributions to the "Physics and Evolution of Symbols and Codes", Howard Pattee of the Binghamton University discussed the seemingly unbridgeable gap (called "epistemic cut") between symbolic structures and dynamic laws implicated in all self-replicating systems, from cellular automata to living cells. Pattee was particularly interested in answering the questions raised in the following quote from J. von Neumann [70]: ". . . By axiomatizing automata in this manner one has thrown (68) half the problem out the window and it may be the more important half. One has resigned oneself not to explain how these parts are made up of real things, specifically, how these parts are made up of actual elementary particles, or even of higher chemical molecules. One dose not ask the most intriguing, exciting, and important question of 1) Why the molecules or aggregates which in nature really occur in these parts are the sort of thing they are? 2) Why they are essentially very large molecules in some cases but large aggregates in other cases? 3) Why they always lie in a range beginning at a few microns and ending at a few decimeter? This is a very peculiar range for an elementary object, since it is, even on a linear scale, at least five powers of ten away from the sizes of really elementary entities." (The rearrangement into a set of questions is my addition.)

It appears that Pattee’s theoretical work did not address the size-related questions raised by von Neumann above. However, possible answers may be formulated on the basis of the conformon theory of molecular machines discussed in Section 5. The following are some of the key ideas suggested by the conformon theory that may lead to answering the questions listed above: 1) The ability for cells to self-replicate is encoded in a set of genes numbering in the hundreds, if not thousands. It should be recalled that the human cell contains approximately 30,000 structural genes constituting only less than 3% of the total DNA mass. The timing of the expression of these genes are controlled by regulatory genes postulated to be ‘encoded’ in ‘non-coding’ regions of DNA constituting more than 90% of the DNA mass [3, 5]. 2) The cell can be viewed as a "supramolecular machine" that is constructed out of a set of n molecular machines, mostly enzymes, but including DNA and RNA, (where n = 106 to 109 ?), each having a diameter about 105 times as large as the diameter of atoms. Interestingly, the diameter of the cell itself is about 105 S. Ji. 97

times as small as the diameter of the human body, suggesting that the cell may possess the right physical size to mediate the world of atoms and that of the mind (see the discussion on micro- and macrosemiotics in Section 8.1). 3) For the cell to be able to self-replicate, it must (i) utilize the free energy provided by chemical reactions which do not proceed inside the cell spontaneously without catalysis performed by molecular machines (also called enzymes), and (ii) control the utilization of free energy in accordance with the genetic information stored in DNA and incoming environmental information. 4) Molecular machines are capable of carrying out both the utilization of the free energy generated from chemical reactions (called "rate-dependent dynamics" by Pattee) and the control of free energy utilization based on genetic information (called "rate-independent genetic symbols”), ultimately because; (i) biopolymers possess the right physical sizes/dimensions to be thermally deformed, thereby transiently storing thermally derived kinetic and potential energies in the form of conformational strains, called "virtual conformons" [34] or "conformational substates" [138], and (ii) the genetic information encoded in the internal structures of biopolymers provide the necessary constraints to synchronize (or control the timing of ) the entrapping of virtual conformons at sequence-specific loci and the catalysis triggered by the virtual conformons, leading to the dissipation of the requisite chemical free energy into heat (thus paying back the thermal energy borrowed from environment quickly enough to avoid violating the second law) [33, 34]. The combination of the synchronized partial processes (i) and (ii) is necessary and sufficient to convert virtual conformons into real conformons. As long as this conversion is completed within the cycling time of the molecular machine, no laws of thermodynamics are violated [196, 35]. It should be pointed out that realistic molecular mechanisms for synchronizing processes (i) and (ii) were proposed almost three decades ago based on the "generalized Franck-Condon principle" [32, 34]. The critical role that thermal noise (also called thermal activations or fluctuations) must play in the workings of molecular motors is now widely recognized [197, 198]. Thus, it appears that the conformon theory of molecular machines formulated between 1972 and 1985 provides reasonably persuasive answers to the questions raised by von Neumann in [70]. Biology (Gnergy Science)

Physics Computer Science (Energy Science) (Information Science) Figure 36. A suggested complementarity between physics and computer science, or more generally between energy science and information science. Since complementarity between A and B presupposes the existence of a third term C (because A and B must be complementary aspects of something), there S. Ji. 98

must exist the C term serving as the source or ground for physics (viewed as A) and computer science (B). Biology, being the science of gnergy, can qualify to be the C term. As pointed out by Pattee [43], we can view the von Neumann questions above as being related to the problem of the epistemic cut between symbolic control/computation and dynamic laws, between rate-independent boundary conditions and rate-dependent dynamics, between the subject and the object, and between symbol (or sign) and matter. To this list of complementary pairs, we may add another pair that I believe embodies an epistemic cut -- computer science and physics, or more generally information science and energy science. This gap may be bridged by biology, the science of conformons (or more generally the science of "gnergy", the complementary union of information and energy): It is hoped that the introduction of the third term C (e.g., Gnergy), into the contemporary discourses on seemingly irreconcilable opposite pairs such as boundary conditions vs. dynamic laws, the subject vs. the object, the mind vs. the body, etc. will contribute to clarifying some of the problems in philosophy and science.

8 Peirce’s Metaphysics as the Basis for Unifying Sciences

Peirce sought to erect a new system of philosophy (or what would now be called a 'Theory of Everything’ (TOE)) based on a simple set of concepts that can accommodate not only the Aristotelian philosophy but also the new knowledge that had accumulated since the time of Aristotle up to the 19th century [17]. He thought that his system of philosophy would apply to all fields of human knowledge, known and yet to be known:

". . .The undertaking which this volume inaugurates is to (69) make a philosophy like that of Aristotle, that is to say, to outline a theory so comprehensive that, for a long time to come, the entire work of human reason, in philosophy of every school and kind, in mathematics, in psychology, in physical science, in history, in sociology, and in whatever other department there may be, shall appear as the filling up of its details. . . . "

To demonstrate the idea that, underlying all phenomena, there are three and only three categories of being, Peirce provided examples belonging to each of these categories in different fields of knowledge of his day (see Table 25). It is highly informative to note the various manifestations of Thirdness in different disciplines. The credibility of his concept of categories seems substantiated by his characterization of biological evolution in terms of chance variations, hereditary transmissions and elimination of the unfit, which seems to be consistent with contemporary theories of evolution. Threeness plays a fundamental role in the metaphysics of Peirce as in the Universe in general (see Section 7.5). According to Peirce, metaphysics is the study of the most general traits of reality. Reality in turn is the object of the conclusions one S. Ji. 99

cannot help drawing. As pointed out by Pierce, “When a mathematical demonstration is clearly apprehended, we are forced to admit the conclusion. It is evident; and we cannot think otherwise.” [15, pp. 47-48].

Table 25. The ontological categories of Peirce and their applications to special sciences, reproduced from [13].

Special Sciences Firstness Secondness Thirdness Phenomenology Quality Fact Law Psychology Feeling Action/Reaction Thought Sensation Perception Belief Physiology Cell Nerve Impulse Habit Excitation Transmission Biology Fortuitous Hereditary Transmission Elimination of Variations of Traits Unfavorable Traits

Metaphysics studies “the kinds of phenomena with which every man’s experience is so saturated that he usually pays no particular attention to them”. Peirce maintained that everything or every phenomenon in the Universe comprises three basic "irreducible" categories or elements -- Firstness, Secondness, and Thirdness. One way to get a feel of these categories is through some of the examples that Peirce gave of these categories throughout his career. These are collected in Table 26, which was adopted from [139]. It is evident that the examples are not logically tight (indeed they are "vague" and even contradictory in some cases), having some overlaps here and there and missing some examples as well. Nevertheless, it is possible to recognize (i) the unmistakable family resemblances among most of the items listed within each category (i.e., within each column), and (ii) distinct family characteristics present among the three categories (i.e., within each row).

Table 26. The evolution of Peirce's nomenclature of categories. Reproduced from [139] except items #8 and #9. Year Firstness Secondness Thirdness (Peirce’s age) 1867 (28) 1. quality relation representation 1891 (52) 2. first second third 3. spontaneity dependence mediation 4. mind matter evolution tendency to take 5. chance law habits S. Ji. 100

6. sporting heredity fixation of character 7. feeling reaction mediation 1894 (55) 8. learning 9. government 1896 (57) 10. quality fact law 1897 (58) 11. ideas of feelings acts of reaction habits 12. quality shock/vividness 13. feeling reaction thought 1898 (59) 14. quality reaction mediation 15. first qualities/ existence/ potential/continuity ideas reaction

8.1 Macro- vs. Microsemiotics

As already indicated, Peirce did not have access to the empirical evidence that came to light only in the mid-20th century: Semiotic processes are not confined to the macroscopic human world but also occur on the molecular level [1-5]. Despite the huge difference in size of the sign processors involved (see Table 27 below), it is amazing to find that there exists a set of principles that is common to the semiotic processes on both the human linguistic and the molecular biological levels (see Table 3 in Section 3.7).

Table 27. A comparison between the physical dimensions of the macroscopic and microscopic sign processors. Notice that the linear dimension of the human body is about five orders of magnitude greater than that of the cell. Adapted from [4].

Parameters Human Body Cell 1. Size Macroscopic Microscopic Linear size (m) ~ 1 ~ 10-5 Volume (m3) ~ 1 ~ 10-15 2. Number of cells involved ~1013 1 3. Signs used for communication Words & sentences Molecules Linear size (m) ~10-3 ~ 10-8 Volume (m3) ~10-9 ~10-24 4. Mechanics obeyed Classical Classical and quantum S. Ji. 101

5. Thermally stable at ~25° C Yes (i.e., rigid) No (i.e., deformable) 6. Powered (or driven) by Chemical reactions Chemical reactions

8.2 Human and Cell Languages as Manifestations of ‘Cosmolanguage’

One support for the notion of semiosis on the molecular level (also called ‘microsemiosis’ [4]) derives from the surprising finding that there exists a common set of rules and principles obeyed by human and cell languages, as summarized in Table 3 in Section 3.7 [1-5]. Cell language is defined as “a self-organizing system of molecules, some of which encode, act as signs for, or trigger, gene-directed cell processes” [1]. This definition of cell language was inspired by the definition of human language given by Saussure [55]: “The language is a system of signs that represent concept”. The former definition can be formally derived from the latter one by applying three main operations: 1) Replace ‘signs’ with ‘molecules’; 2) replace ‘systems’ with ‘self-organizing systems’; and 3) replace ‘concepts’ with ‘gene-directed cell processes’ (See Figure 36a).

“The language is a system of signs that represent concept.”

| | 1) Signs => Molecules | 2) Systems => Self-Organizing Systems | 3) Concepts => Gene-Directed Cell | Processes | \/

“Cell language is a self-organizing system of molecules, some of which encode, act as signs for, or trigger, gene-directed cell processes.”

Figure 37. The ‘formal’ derivation of the definition of cell language from that of human language given by Saussure [5, 55].

The proposition that cell possess their own language, ‘cell language’, seems almost tautological to the fat that cells communicate. So there is no doubt that cell language must and does exist. The natural question that then arises is the question concerning the possible relation between human language and cell languages. There may be three main possibilities: 1) Human language has evolved from cell language. S. Ji. 102

2) Both cell and human languages are different manifestations of a third language that exists independent of, and serves as the source of, them. 3) Possibilities 1) and 2) are not mutually exclusive but represent the diachronic and synchronic aspects of the fundamental characteristics of the Universe we inhabit, namely, that the final cause of our Universe is to Know Itself through Homo sapiens. Such a Universe was named the Self-Knowing Universe or Universum sapiens in 1991 [38, pp. 236-237] (see Section 10).

Thus, by accepting the ultimate hypothesis that we are living in the Self-Knowing Universe, we can rationalize the existence of both cell and human languages as diachronic manifestations of a third language which may be referred to as the cosmological language (or Cosmolanguage, for short). By invoking the existence of cosmolanguage, I am in effect postulating that the language principle (or more generally semiotic principles) apply to all phenomena in this Universe. In 2000, I expressed the same conclusion as follows [see Appendix X and XI]:

“. . . the principles of language (and associated semiotic principles of Peirce, including rule-governed creativity and double articulation) are manifested at two levels – at the material level in the external world as well as at the mental level in the internal world. We may refer to this phenomenon as the ‘principle of the dual manifestations of language or semiosic principles’, or the ‘language duality’ for short. Like the wave/particle duality in physics, this matter/mind duality may be a reflection of a deep-lying complementarity which may be identified with the following triad . . . ” (see Figure 38):

Cosmolanguage

Material Language Mental Language

Figure 38. The postulate that the cosmolanguage is manifest in two ways – externally as material language (including cell language) and internally as mental language (exclusive to Homo sapiens ?). Adapted from Appendix X.

Figure 38 can be read in two ways – diachronically (or ontologically) as indicating the evolution of the mental and material languages from the cosmolanguage, and synchronically (or epistemologically) as indicating the material and mental languages as complementary aspects of the cosmolanguage. Both these interpretations are consistent with the model of the Universe called the Shillongator proposed in 1986 [38] (see Section 10.1).

S. Ji. 103

8.3 Complementarism and Semiotics

The formulation in the early 1990's [11, 30] of the philosophical framework known as complementarism was motivated by the possibility that the principle of energy/information complementarity (see Section 3) found to account for the mechanisms of molecular machines and the living state may be extended to understanding the origin of the nonliving state [38]. The physicometaphysical entity for which energy and information are complementary aspects in the Bohrian sense (see Appendix I) was referred to as "gnergy" as already indicated (see Section 3.1). Discrete units of gnergy were called gnergons, which consist of information 'particles' (gnons) and energy 'particles' (ergons). The first concrete example of gnergons was provided by conformons resident in DNA (see Section 5.1). In the mid- to the late-1800's, Peirce constructed a general theory applicable to both life and nonlife that was based on his triadic metaphysical doctrine of Firstness, Secondness, and Thirdness (see Table 23), and the associated semiotics [12-17]. Since both complementarism and Peircean semiotics represent theories of everything (TOE), the former based on the science and philosophies of the 20th-21st century and the latter based on those of the 19th century, it may be instructive to compare these two systems of thought side by side:

Table 28. A comparison between complementarism and semiotics.

Semiotics Complementarism

1. Formulated in The 19th Century The 20th-21st Centuries

2. Origin Philosophy & Logic Biology & Physics

3. Basic Tendency for habit-taking Gnergy is necessary and sufficient Premise applies to both life and nonlife. to account for both nonlife and life.

4. Metaphysics There are three (and only three) Energy and Information are the fundamental aspects to all complementary aspects of a third phenomena – Firstness, entity, Gnergy, the ultimate cause Secondness, and Thirdness of all phenomena in the Universe.

5. Key Concepts Signs Gnergons [see Section 10.4 & Appendix IX]

6. Adicity Triadic: Firstness, Secondness Triadic: Gnergy, Energy, and and Thirdness Information S. Ji. 104

Dyadic: Energy and matter; Information and Life (see Section 10.4)

7. Principle The universality of triadicity Complementarity [11, 30] & Supplementarity (see Appendix I and Section 10.4)

8. Consistent Christian triune doctrine Taoist Philosophy (6th Century BC) with (God-Son-Spirit) (Tao-Yin-Yang) [193]

Aristotle (384-322 BC) (Hylomorph-Matter-Form) [27]

Spinoza (1632-1677) (Substance-Extension-Thought) [28, 188]

C. S. Peirce (1839-1914) (Firstness, Secondness, & Thirdness) [12-17]

M. Merleau-Ponty (1907-1968) (Flesh-Body-Mind) [29]

9. Range of Macroscopic From microscopic to macroscopic Applications (Philosophy, logic, (e.g., from molecular machines in mathematics, etc.) living cells to the Bing Bang)

It is clear that there is a considerable degree of family resemblance between the second and the third columns in Table 28. One interesting feature is that Peircean semiotics is enfolded within complementarism (as evident in Row 8). If the content of Table 28 is correct, complementarism and Peirce's semiotics may be viewed as belonging to the same lineage of philosophical systems: Complementarism and semiotics may be homologous. In fact, complementarsim may be considered as a descendant of, and encompasses, Peirce's semiotics, just as quantum mechanics is regarded as a descendant of, and encompasses, Newtonian mechanics. Within the validity of the content of Table 28, we may derive following conclusions:

1) Some of the many uncertainties and confusions arising in the hermeneutics of Peirce’s semiotic writings may be clarified by using the relatively clear-cut set of the principles embodied in complementarism (e.g., see the complementarian logic in Section 3.3 and Appendix VIII ), just as some phylogenetic classification S. Ji. 105

problems of organisms can now be resolved based on molecular biological data. 2) The divisions between biology and physics on the one hand and between natural sciences and human sciences (including linguistics, philosophy, and religion) on the other may be viewed as artifacts of the human tendency/necessity to compartmentalize and specialize for the convenience of learning and teaching. These divisions may be eventually removed for a deeper understanding of Nature and Homo sapiens in it. 3) Many perplexing problems faced by the contemporary natural sciences (e.g., in molecular biology, the problems of protein folding, and mechanisms of force generation in molecular motors and of gene expression, etc.) may not be resolved without the general guidance provided by sound metaphysical presuppositions underlying scientific research. Natural scientific problems may be too difficult to be solved within natural science (entailing assistance from philosophers), just as some philosophical problems may be too difficult to be solved within philosophy (entailing assistance from natural scientists).

8.4 Signs, Thoughts, and ‘Thoughtons’

According to Peirce, "We have no power of thinking without signs" [140]. That is, WE THINK in SIGNS. (70) There are three key terms in Statement (70): WE = a biological sign processor (see Table 2 in [53]); THINK = a process driven by free energy, and SIGN = the mechanism or process through which the sign processor think or compute. The main objective of this section is to propose that it is possible to conceive of the basic unit of thoughts, to be called "thoughtons", that contain all of the three components listed above, namely, P = the sign processor, E = the free energy necessary to drive thinking process, and S = the sign that makes it possible for the sign processor to carry out thinking. As will be explained below, Peirce's well-known definition of signs seems to emphasize mainly S; Spink's definition of signs [53, 54, 141] expanded Peirce's definition by including P; and the definition of signs that I will propose below will contain all of the components, P, E and S. Peirce geometrically represented a sign using the triple fork icon , --< : I R --< O Figure 39. Peirce's sign triad. R = representamen, O = object, and I = interpretant (i.e., the effect that a sign has on an interpreter or a sign processor). Note that a sign is triadic in the S. Ji. 106

sense that it cannot be reduced to R, O, or I, or to any partial combinations of these (see Figure 1; the bracket symbolizes the irreducibility of the sign triad).

In 1981, C. W. Spinks 3-dimensionalized Peirce's 2-dimensional sign triad by treating the center of the triple fork as the apex of a pyramid raised over the triangular base whose vertices are occupied by R, O and I [53, 54, 141]:

X /|\ / | \ O /_ _|_ _\ I \ | / \ | / \/ R Figure 40. Spink's sign pyramid. The base of the pyramid is identical with Peirce's sign triad shown in Figure 1. The new element, X, stands for either Producer or Receiver of signs [141], depending on the context of the discourse. The horizontal line is meant to be behind the vertical line; the triangle formed by R, O and I serves as the base of the pyramid; and X as its apex.

The pyramidal structure shown in Figure 40 is best treated as a tetrahedron, as Spinks himself sometimes does (see p. 92 in [53]), since a pyramid can have as its base a shape other than a triangle, whereas a tetrahedron cannot have surfaces that are not triangles. In analogy to Peirce's "sign triad", the structure in Figure 40 may be referred to as Spink's sign pyramid", "sign tetrad", or "sign tetrahedron". I have been using the body-centered tetrahedron in my writings since 1991 as a geometric representation of either self-organizing systems driven by gnergy (see Figure 1.14 in [38] and the 'Tarragonator' in [51]) or gnergy itself. Gnergy, placed at the center of the so-called gnergy tetrahedron, is postulated to embody the complementarity between information (I) and energy (E), each of which in turn representing the information/life and energy/matter supplementarities, respectively (see Section 10.4). Encouraged and stimulated by Spink's sign tetrahedron (Figure 40), I have been led to the idea that Peircean sign triad may be further modified beyond the tetrahedron to a "body-centered tetrahedron" (BCT) as shown in Figure 41:

S. Ji. 107

P | | | | _ G _ / \ _ / \ O / \ / I R

Figure 41. The Peircean sign depicted as a body-centered tetrahedron (BCT), which represents the unit of thought called the “thoughton”. G = Gnergy; P = Sign Processor; R = Representamen; O = object; and I = Interpretant.

The four “bonds" emanating from G in Figure 41 are tetrahedral, reminiscent of the four covalent bonds centered at the carbon nucleus. The GP and GI "bonds" lie on the plane of the paper, while the GR and GO "bonds" are, respectively, above and below the plane of the paper. Evidently, the BCT model of the sign is identical with Spink's sign tetrahedron, except (i) that X is replaced with P, the sign processor, the need for which was emphasized in [53], and (ii) that Gnergy is explicitly introduced as a new element of the sign, providing the requisite thermodynamic driving force for sign processes or semiosis. It is obvious that to invoke sign processes (and thought processes) without identifying the source of free energy violates the laws of thermodynamics. Also, it is essential that the sign processor, P, be explicitly indicated in all sign processes, since, as Spinks correctly pointed out, the semantics of a sign is critically dependent on the nature of P, without which the semantic space vanishes [53, 54, 141]. It is suggested here that BCT shown in Figure 41 represents the most complete description of the sign so far formulated, taking into account not only the informational aspect of signs as emphasized by traditional semioticians, including Peirce and Spinks, but also the energetic/dynamic aspect whose importance having been clearly recognized with the emergence of microsemiotics, the study of molecular signs [5]. The content of BCT in Figure 41 can be algebraically represented as a 5-tuple, (G, P, R, O, I), which provides a convenient way of writing down the definition of the thoughton: Thoughton = (G, P, R, O, I) (71) It may be suggested that thoughts can be represented as sets of two or more thoughtons organized into networks with specific patterns of interactions and degrees of coupling: Thoughts = (T, Ed, S) (72) where T stands for a set of thoughtons, each of which is defined by (71), Ed is the set of edges connecting thoughtons acting as nodes, and S is the set of the coupling strengths, S. Ji. 108 each assigned to an edge. It should be possible to represent Eq. (72) as an adjacency matrix [142] widely used in fuzzy set theory. Finally, it is suggested that the BCT model of the sign given in Figure 41 is consistent with the Peircean categories of Firstness, Secondness, and Thirdness thus: Firstness = G as the absolute origin of everything in the Universe and the motive force for all self-organization, including sign processes or semiosis going on inside the human brain. Secondness = G(P, R, O, I), i.e., the reification or actualization of G into P, R, O, and I here and now. Thirdness = The cosmological language enabling humans to communicate among themselves as well as with the Universe at large.

8.5 A Topological Theory of the Mind

The recent writings by S. Pinker[143], F. Crick [144], and G. Marcus [145] have made significant contributions to establishing the material basis of the mind, as formulated in the following three propositions. To facilitate discussions, I have labeled these propositions as indicated. The Pinker thesis: "The mind is what the brain does." (73) The Crick thesis: "The brain is what cells and biochemicals do." (74) The Marcus thesis:

"The cell is what genes do." (75) The purpose of this Section is to outline a theory of mind that combines the 5 conceptual elements embedded in the above three propositions and a topological relation previously called "Möbius pentad" [146, 147; also see Section 3.5]: 1) The 5 elements are Mind, Brain, Cells, Genes, & Biochemicals (i.e., organic molecules with small molecular weights such as glucose, pyruvate, NADH, and ATP, and inorganic ions; biochemicals are the source of the free energy and information that are essential for maintaining life). 2) A Möbius pentad [146] is defined as the relation among 5 elements, A, B, C, D, and E that obeys the following rule: "Locally A, B, C, D or E; globally A, B, C, D, and E." 3) The theory proposed herein results from substituting A with Mind, B with Brain, C with Cells, D with Genes, and E with Biochemicals. The resulting theory then reads; S. Ji. 109

"The mind is a Möbius pentad consisting of the mind, (76) the brain, cells, genes, and biochemicals." Alternatively, "The mind, the brain, cells, genes, and biochemicals (77) constitute a Möbius pentad."

Paraphrasing, we can say "When viewed locally, we see only the mind, the brain, (78) cells, genes, or biochemicals (e.g., neurotransmitters); but when viewed globally, the mind is indistinguishable from the brain, which is indistinguishable from cells and neurons, which are indistinguishable from genes, which are indistinguishable from biochemicals, which, finally, are indistinguishable from the mind, thus leading to what may be called the Möbius closure." To me the most unexpected feature of Statement (78) is the penultimate clause asserting the indistinguishability between biochemicals and the mind, which is a logical consequence of applying the topological principle of the Möbius pentad to the phenomenon of the mind. The topological theory described here can be represented diagrammatically as shown in Figure 42 using a "body-centered tetrahedron" where the center of gravity of the tetrahedron is occupied by Biochemicals, and the four apexes by the remaining 4 elements of the Möbius pentad. The rationales for locating Biochemicals at the center of gravity of the tetrahedron include the facts (i) that, without the free energy derived from biochemicals, no life is possible, and (ii) that, ontologically, biochemicals preceded and hence are prior to all other members of the pentad. Genes probably appeared on the surface of the Earth before cells, according to the Princetonator model of biological information (see Section 7.5). The appearance of the brain then followed that of cells, and, after the invention of spoken language, mind must have emerged. Genes (2) | | | | Biochemicals (1) - / \ - / \ Mind (5) - / \ / \ Brain (4) Cells (3)

Figure 42. The topological theory of mind (the New Jersey Theory of Mind) based on a Möbius pentad represented as "body-centered tetrahedron". Biochemicals are at the S. Ji. 110 center of the tetrahedron, and the four spokes emanate from the center towards the four apexes. The numbers indicate the postulated order in which the different vertices emerged on this earth

In analogy to the so-called "Santiago Theory of Cognition" formulated by H. Maturing and F. Varela based on G. Bate son’s suggestions [148], the theory of mind depicted in Figure 42 may be referred to as the "New Jersey Theory of Mind" to acknowledge my indebtedness to the residents of the State of New Jersey, whose tax moneys have been supporting my teaching position at Rutgers for the past 23 years, during which time I have been able to carry out my theoretical research in biology, leading to the formulation of the theory of mind presented here. The connection between the New Jersey theory of mind and the three theses described in (73) through (75) above can be represented schematically as shown in Figure 43. Pinker thesis + Crick thesis + Marcus thesis | | + the Möbius pentad | \/ The New Jersey Theory of Mind

Figure 43. The New Jersey theory of mind as an integration of the Pinker, Crick and Marcus theses on the basis of the topological principle of the Möbius pentad. The New Jersey theory of mind is unique in (i) that, to the best of my knowledge, it takes into account and is consistent with all of the contemporary molecular biological and neurobiological knowledge concerning the material basis of the brain, (ii) that it is based on a topological principle formulated as a generalization of the properties of the Möbius band visualized as the body-centered tetrahedron, and (iii) the same topological principle underlying the NJ Theory of Mind was also applied to living cells, the human body, Peircean signs, and the Universe (see Section 10.7).

8.6 The Cognitive Uncertainty Principle

There seems to be an intrinsic limit to the ability of human mind to acquire accurate information about the world. The human mind may be subject to the intrinsic cognitive inability to see both trees (i.e., depth) and the forest (i.e., breadth) simultaneously. This idea naturally reminds us of the Heisenberg uncertainty principle (HUP) in quantum physics, and hence it is here suggested that an analogous principle exists for cognitive science. This analogue may be referred to as the "cognitive uncertainty principle (CUP)". HUP and CUP are compared in Table 29.

Table 29. The definition of the "Cognitive Uncertainty Principle" in analogy to the Heisenberg Uncertainty Principle. S. Ji. 111

Heisenberg Uncertainty Principle Cognitive Uncertainty Principle (HUP) (CUP)

1. Formula q x p  h/2π B x D  K

2. Conjugate Position (q) Forest or Breadth (B) Variables Momentum (p) Trees or Depth (D)

3. Universal Constant h (Planck constant) K ("cognitive constant"?)

4. Fundamental MEASUREMENT COGNITION Process (Measurement by artificial devices) (Measurement by the human brain )

5. Logic [11] (see Complementarian Logic: Complementarian Logic: Appendix VIII) A = Position A = Breadth B = Momentum B = Depth C = Light or Quons C = Reality

B and D in the first row of the third column refer to the uncertainties about B and D, respectively, and K indicates a constant that delimits the accuracy of our cognitive power which may be called "cognitive constant". There is a pleasing 'family resemblance' between the second and the third columns with respect to the fundamental processes involved: Just as measurement is performed using artificial devices, so cognition is carried out by natural devices known as the human brain. Finally, it is suggested in the last row that both HUP and CUP obey the complementarian logic [11, p. 524] (see Figure 5 in Section 3.3 ). As shown in the last row of Table 29, the values of the logical variables A, B, and C vary, depending on whether HUP or CUP is under discussion. The C term of HUP represents the quantum world, while that of CUP may represent reality, both quantum and classical, as perceived by the human mind.

9 The Information-Energy Relation

One of the fundamental assumptions made in formulating the unified theory of molecular machines, living cells, mind, Peircean signs, and the Universe advanced here is that information (simply defined as the ability to select given energy or the record of having been selected, dissipating the requisite free energy) and energy (the ability to do work, including selecting) are complementary aspects of a third entity called gnergy [38] (see Section 3.1). But the complementary relation between information and energy is not generally accepted among scholars, and there are other possible relations as well (see S. Ji. 112

Table 1 in Section 2.1 related discussions). There are three schools of thoughts concerning the relation between energy and information (cf. Table 1): 1) The monadic school; (i) Energy and information are identical. Brillouin’s “Negentropy Principle of Information” may represent the most widely discussed example of this school of thought [23, 24, 149]. (ii) Energy is primary and information is derivable from it [19-22] (iii) Information is primary and energy can be derived from it [18].

2) The dyadic school; Energy and information are two separate and distinct primary entities on an equal footing. Two variations of this school may be recognized— (i) Energy and information are distinct and cannot be interconverted. Bohmian quantum mechanics [150] and Laszlo’s “connectivity hypothesis” [151] may belong to this school. (ii) Energy and information are distinct but can be interconverted to each other. [25, 26]. 3) The triadic school; Energy and information are the two complementary aspects of a third entity. Spinoza’s and Merleau-Ponty’s ontologies seem to exemplify this school of thought. Spinoza [28, 188] referred to the third entity as Substance (also called Nature or God), while Merleau- Ponty referred to it as Flesh [29]

My own preference obviously is 3), because this choice is supported by the following facts: 1) The units of energy (e.g., Kcal/mole) and information (e.g., bits) are different. 2) The energy of the Universe is constant (the first law of thermodynamics) but the information content of the Universe may (and need) not be. 3) Energy is represented as a vector field while information is associated with the scalar field of the cosmic plenum, according to Laszlo [151]. 4) In molecular machines in action, energy and information are indistinguishably intertwined into one entity called conformons (i.e., sequence-specific conformational strains of polymers)[34]. Conformons can be viewed as specific instantiations of gnergy in the living cell. The conformon concept was supported by the results of the statistical mechanical analysis of supercoiled DNA double helices in bacteria. These results indicate that gene expression requires the storage of mechanical energies in sequence-specific sites within DNA duplexes, and such sequence-specific DNA deformations are called SIDDs (stress-induced DNA duplex destabilizations) [77, 78, 195].

9.1 A ‘Philosophical Table’ for Classifying Informations, Entropies, and Energies

The concept of entropy was introduced by R. Clausius (1822-1888) in thermodynamics as a so-called state function of a thermodynamic system, namely, a number that characterizes the physical state of the system independent of its past history [50]. This thermodynamic concept was then extended to the field of information theory S. Ji. 113 by C. Shannon (1916-2001) in 1945 who, at the (somewhat haphazard) suggestion of von Neumann, named his quantitative measure of information ‘entropy’ (hence also known as Shannon entropy or information-theoretical entropy). A small number of physicists recently proposed that the basic laws of physics may be derived based on the concept of information. Caticha [152] derived the equations of quantum mechanics based on Shannon information and the principle of maximum entropy. R. Frieden [153] derived many basic laws of physics starting from Fisher information, including the equations of classical mechanics, quantum mechanics, and general relativity. In these and related writings, the terms 'information' and 'entropy' tend to get used interchangeably without any rigorous definition of the relation between them. Such loose usages of the terms may be harmless under most situations in mathematics and computer science but may lead to serious problems in physics, philosophy of science, and biology, especially when information is claimed to be linked to the entropy of the second law of thermodynamics (for a critical review of this problem, see [154]). This is why I think it is imperative that we have some rational basis of classifying the various versions of entropies and informations widely discussed in contemporary literature in science and philosophy. The purpose of this section is to suggest a possible philosophical framework to classify informations and entropies. It is suggested here that there are three distinct classes of entropies -- thermodynamic, statistical mechanical, and mathematical. The philosophical framework that can provide a rational basis for classifying informations and entropies is constructed from the following three logical elements: 1) The complementarian logic. The principle of generalized complementarity states that the ultimate reality, C, is a complementary union of irreconcilably opposite pair, A and B. When A and B are energy and information, C is referred to as gnergy [38]. (See Figure 4 in Section 3.2). 2) The triadic logic of Peirce [12-14]. All phenomena exhibit three basic, irreducible and inseparable, elements: a) Firstness, b) Secondess, and c) Thirdness (see Table 23). 3) 3) The principle of recursion or recursivity (see Section 3.12 and [65]). The complementarian logic and Peirce's triadic logic are inseparably (or recursively) intertwined like the two surfaces of the Möbius band.

The structure of the philosophical framework emerging from these elements can be represented as a 4x4 table having the following characteristics: a) The top row (i.e., the horizontal vector) consists of E1, E2, and E3, where E stands for energy and the Arabic numerals refer to the Peircean categories. b) The left-most column (i.e., the vertical vector) consists of I1, I2, and I3, where I stands for information and the Arabic numerals refer to the Peircean categories. c) The diagonal array consists of G1, G2, and G3, where G stands for gnergy and the Arabic numerals refer to the Peircean categories. d) The row and column vectors are orthogonal and complementary, i.e., they are independent of each other with no direct interactions nor interconversions allowed. e) The diagonal array, G, cannot be directly measured; only its complementary components, I or E, can be empirically measured.

S. Ji. 114

Table 30. A philosophical analog of the periodic table constructed on the basis of the complementarian logic and Peircean categories. Please note that Ii and Ei are orthogonal to each other on the one hand and constitute the complementary aspects of Gi on the other, where i is 1, 2, or 3.

E1 E2 E3 (Empty) Matter (Aristotle) Lawful behaviors (or Extension (Spinoza) Laws) of matter and Body (Merleau- energy in physics, Ponty) chemistry, and Thermodynamic (or biology, including the Clausius) entropy Second Law of Conformational thermodynamics. strains of DNA [34, 38, 76, 77, 78, 195]

I1 G1 (Empty) Potentialities (Peirce)[155, pp. 24-34] Gnergy as Firstness

I2 G2 Form (Aristotle) Concrete Thought (Spinoza) Substance (Aristotle) Mind (Merleau-Ponty) Substance (Spinoza) Actualities (Peirce) Boltzmann entropy DNA sequence [155] Information [156] Flesh (Merleau-Ponty) Bohm’s Implicate Order Gnergy as Secondness [150] (e.g., conformons, action Laszlo’s scalar potentials potentials) of cosmic plenum [151]

I3 G3 Generalities (Peirce) Gnergy as Thirdness [155, pp. 34-42] Evolving Universe Fisher information[153] Habits Hartley information[91] Shannon entropy or information [91] Tsalis entropy [157] S. Ji. 115

Of the 9 boxes generated in Table 30, only 7 (i.e., the boxes labeled G1, G2, I2, E2, G3, I3, and E3) are occupied by phenomena, physical entities, concepts, or laws from philosophy, physics, and biology. This is because I1 and E1 are empty due to the fact that G1 is the Firstness of Peirce, which, by definition, means that G1 is incapable of being described in terms of others, including I1 and E1. If such descriptions were to be possible, G1 would no longer be First but Second to I1 and E1. Thermodynamic entropy, S, is treated as a member of the E2 group, because S constitutes an essential component of free energy as evident from the formula of free energy given by G = E + PV – TS. I regard Boltzmann entropy as an example of ‘objective’ information existing independent of human mind, similar to the ‘physical information’ stored in the form of nucleotide sequences in DNA as discussed by Adami [156], to the ‘in-formation’ of Bohm [150] pervading the Universe nonlocally, and to Laszlo’s ‘scalar potentials’ embedded in the cosmic plenum [151] . In contrast, Shannon information and other types of informations defined in physics, computer science, and statistics are viewed as ‘subjective’ and hence grouped together as members of I3. On the basis of Table 30, we can make the following deductions: 1) There are three kinds of entropies, belonging to I2 (i.e., Boltzmann entropy), E2 (Clausius entropy), and I3 groups (e.g., Shannon entropy), which can be identified with what were previously referred to as “statistical mechanical”, thermodynamic”, and “mathematical” entropies, respectively [158]. 2) There are two kinds of informations, belonging to the I2 group (e.g., DNA sequence Information, Bohm’s vector potentials, also called quantum potentials, and Laszlo’s scalar potentials of cosmic plenum ) or to the I3 group (e.g., Fisher information). I2 is objective and I3 subjective, in the sense that the former is independent of, and the latter dependent on, the emergence of Homo sapience on this planet. 3) I2 and I3 entropies can increase with time, but these increases are not the physical consequence of the Second Law but merely its simulations or descriptions. Therefore, there can be many mathematical expressions that can simulate the Second Law as long as they show a monotonic increase with time. In contrast, the increase of E2 entropy with time is a direct physical consequence of the Second Law. In other words, there are two kinds of entropies -- the Second Law– obeying and the Second Law-independent entropies, the E2 entropy belonging to the former and I2 and I3 entropies belong to the latter. If this conclusion is right, the validity of NPI (Negentropy Principle of Information) of Brillouin may be in doubt. [23, 149].

9.2 The Information-Energy-Entropy Relation: The ‘NewJerseyator’

Klir [91] discussed two kinds of information -- i) "algorithmic" or "descriptive" information and "uncertainty-based" information. Algorithmic information "represented by an object is measured by the length of the shortest possible program written in some standard language by which the object is described in the sense that it can be computed". Uncertainty-based information can be defined as any signal that reduces uncertainty. If the signal reduces the uncertainty measured in terms of Shannon's entropy function, H = - pi log2 pi, then "Shannon information" is involved. If the uncertainty is measured in S. Ji. 116

terms of Hartley function, U(X) = log2 |X|, where X is a set of elements and |X| is the number of the elements in X (or the cardinality of set X), then Hartley information" is involved. The relation between uncertainty-based information and uncertainty (alternatively called 'entropy' or 'disorder') is clear:

Information = Uncertaintybefore - Uncertaintyafter (79) where ‘before’ and ‘after’ are relative to the time of the action of selection universally involved in information production (or ‘measurement’) or utilization (or ‘control’ [43]). That is, according to Eq. (79), information is equivalent to "uncertainty reduction" resulting from some selective action. We can also represent Eq. (79) diagrammatically as follows: Action (I) Uncertaintybefore ------> Uncertaintyafter Figure 44. A theoretical model of the relationships among information, I, uncertainty (entropy, disorder, intropy), and energy (action, free energy). Action (I) denotes any free energy-consuming process generating information, I, or utilizing I that is associated with the reduction on the initial uncertainty to the final one. The simple model shown in Figure 42 represents the fundamental relations among the three primary ingredients (two of which having alternative labels) that are involved in any transactions of uncertainty-based information: i) uncertainty, ii) ii) information, and iii) iii) action (including selection, measurement, and control, all supported by free energy dissipation).

In view of the universal applicability of the model in Figure 42 to all discourses on information, I have taken the liberty of referring to it as the "NewJerseyator", sp that we can acknowledge the contributions made by Shannon who developed his information theory while working at IBM and by von Neumann who was at the Institute for Advanced Studies, both located in the State of New Jersey, which also supported my research at Rutgers directly or indirectly for the past twenty three years. The neologism of yet another "ator" (according to Stan Salthe, there are “Toomanyators" already) may be justified, if the NewJerseyator can provide a logical framework to resolve many of the confusions that have been afflicting the interdisciplinary field between physics, biology and computer science, where information and energy play the key roles. As many have recognized, there are three "orthogonal" aspects to information -- i) quantity, ii) meaning, and iii) value. The NewJerseyator was originally conceived of in order to resolve mainly the quantitative aspect of information. It would be interesting to see if it can also help resolve problems associated with the other two aspects of information. Let us ask the NewJerseyator what the expression "information is equal to order" means. The answer suggested by the NewJerseyator is that it does not have any meaning, because you cannot identify "information" with "order". The reason is that "order" is not S. Ji. 117 a "difference" but rather a single term and that at least two single terms are required to generate a difference. Information can be equated with a positive difference between the initial (before selection) and final (after selection) uncertainties [see Eq. (79) above]. No single term on the right-hand side of Eq. (79), either ordered or disordered (uncertainty), can qualify as information. According to the NewJerseyator, both of the following differences can be associated with (or with the action of ) information:

I 1) Disordered ------> Ordered = Increased order

I 2) Ordered ------> Disordered = Decreased order

Case 1) is what people usually have in mind, but, according to the NewJerseyator, Case 2) can also be associated with information. If this analysis is right, the concept of negative information must be admitted, since applying Eq (79) to Case 2) logically lead to it which can be interpreted as a loss of information. In other words, Case 1) entails an increase in information, while Case 2) entails a decrease in information.

9.3 Semiotics and Information Theory

The study of information may not be successfully carried out without the aid of semiotics. This is because information is carried by signs (i.e., without signs, no information can be generated, transformed, or transmitted) and the study of signs in general is the domain of semiotics. As evident in the following quotations, D. Nauta [159] came to a similar conclusion: ". . . Much work has been done in the field of pure information theory, but the problems concerning the meaning (i.e., semantics vis-à-vis syntactics; my addition) and application (i.e., pragmatics) of information have largely been neglected. In our opinion, these important problems can be tackled only from a semiotic point of view. The key to these problems will be the analysis of signals, signs and symbols." [159, p. 29] "Semiotics, divided into transmission theory, syntactics, semantics and pragmatics, and subdivided into pure, descriptive, and applied semiotics, offers a general framework for the study of information processes and for the development of a universal theory of information. In its generalized form, semiotics encompasses the following fields: Logistics (artificial symbols) Linguistics (symbols) Semiotics in a narrower sense (signs) Automatics, the study of automatic processes and pre-coded representations and mechanisms (signals)." [159, pp. 61-62] S. Ji. 118

Nauta distinguishes a triad of information carriers -- "signals", "signs", and "symbols". He defines signals as carriers of form but not meaning nor function; signs as carriers of form and meaning but not of function; symbols as carriers of form, meaning and functions (see Diagram 2C on p. 38 in [159]). This contrasts with Peirce’s' division of signs into "iconic signs", "indexical signs", and "symbolic signs", each of which can have form, meaning, and function.

Table 31. Definition of signals, signs and symbols according to Nauta [159].

Form Meaning Function Signals + - - Signs + + - Symbols + + +

It is not clear why it was necessary for Nauta to invoke his triad of information carriers rather than using Peirce's original sign triad, but it may be possible to represent Nauta's information carriers as linear combinations of Peirce's triadic signs. Writing Nauta's information carriers with capital letters and Peirce's signs with lower-case letters, we may construct a set of algebraic equations as shown below, where doubly indexed coefficients, aij, indicate the degree of contribution of Peircean signs to a given information carrier (IC) of Nauta:

Signal = IC1 = a11 icon + a12 index + a13 symbol (80) Sign = IC2 = a21 icon + a22 index + a23 symbol Symbol = IC3 = a31 icon + a32 index + a33 symbol In general, we may write: IC = Ť x S (81) where IC is Nauta's information carrier vector (a column vector consisting of IC1, IC2 and IC3), Ť is the transpose of the 3x3 transformation matrix consisting of 9 aij's as they appear in (80), and S is the Peirce's sign vector (another column vector consisting of Icon, Index and Symbol). Equation (81) may be viewed as an algebraic expression of the "fundamental relation between information theory (as represented by IC) and semiotics (as represented by S)", or alternatively as a projection of the "information space" onto what may be referred to as the "Peirce space” of signs. More recently Debrock (pp. 79-89 in [139]) proposed a novel theory of information viewing information as events rather than as entities and suggested that such a dynamic approach to information may be consistent with the Peirce’s theory of signs. Debrock’s suggestion seems consistent with the postulate that Peircean signs are gnergons, the driving force for all self-organizing processes including informational events (see Figure 42 and Section 8.4).

S. Ji. 119

Part V The Universe

10 A Model of the Universe based on the Gnergy Tetrahedron

Biologists usually consider cosmology largely irrelevant to their field of study, except those few biologists who are interested in investigating the origin of life. But it is my opinion that any fundamental theory of life that attempts to explain the phenomenon of life as is here and now not only must be consistent with but also must contribute to formulating the theories of the origin of our Universe in which life exists. In other words, no theory of life may be considered complete if it does not provide some explanation as to how the Universe originated some 15 billion years ago and how life originated in it about 12 billion years later in addition to explaining how living processes work here and now on the molecular level.

10.1 The Shillongator Model of the Universe

The body-centered tetrahedron (BCT) possesses a topology that is useful in visualizing some of the most profound physical insights we can have about the Universe. There are two ways of interpreting the topology of BCT – the diachronic and synchronic interpretations. In the synchronic interpretation, the four vertices are assumed to be mutually interacting either via edges or through the center of BCT. The focus here is on the interactions among the vertices, actual or virtual, rather than on their historical dimensions. In contrast, in the diachronic reading of BCT, the focus is placed on the order of the emergence of the four vertices from the center of BCT. It is assumed that the center emerged first (from Firstness of Peirce, I presume), followed by the four vertices in the order of their labels, as shown in Figure 45: (2) | | | | (1) - / \ - / \ (5) - / \ / \ (3) (4) Figure 45. A body-centered tetrahedron represented as a symmetrically distributed set of four spokes or bonds. The four vertices are thought to have emerged from the center, (1), in the order, (2), (3), (4), and (5). In 1991 [38, pp. 230-237], a theoretical model of the origin of the Universe was proposed on the basis of the concept of the gnergy tetrahedron (see Figure 46 below). S. Ji. 120

Figure 46. A theoretical model of the Universe based on the concept of gnergy tetrahedron [38, pp. 230-237]. Gnergy, namely, the complementary union of information (gn-) and energy (- ergy), can be thought of being located at the center of the tetrahedron, out of which four vertices emerged in the following order: Energy, Matter, Information, and Life. The circle composed of the 5 solid arrows and one dotted arrow can be viewed as a description of the evolution of the gnergy tetrahedron, including the emergence of life, Homo sapiens, and human culture. For a more detailed account of this figure, see pp. 234-237 in [38].

When the gnergy concept was first invoked to account for life on the molecular and cellular levels, the emphasis was on the complementarity between information and energy (or gnergy), but when an attempt was made to extend the gnergy concept to the origin of the Universe, it became evident that the information-energy complementarity (which can be represented as a triangle comprising three vertices of Gnergy, Energy, and Information) needed to be augmented by supplementarity relations between energy and matter on the one hand and between information and life on the other. (See Appendix I for the definitions of complementarity and supplementarity given by Bohr.) The supplementarity relation between energy and matter is given by the well-known equation S. Ji. 121 of Einstein, E = mc2, as already pointed out. This led me to consider the possibility that a similar relation may hold between information and life. Thus, I found myself asking the following question. Just as physicists consider matter as highly concentrated energy, can life be considered as highly concentrated information? Having found no legitimate reason to exclude this possibility, I decided to accept as a hypothesis the supplementarity relation between information and life in analogy to the supplementarity relation between energy and matter [38, p. 234]. To represent the combined complementarity and supplementarity relations diagrammatically, I chose the tetrahedron, which is the 3- dimenwsional analog of the triangle, both being the simplexes (i.e., simplest polygons) in the 2- and 3-dimensional spaces, respectively [126, p. 146, Volume III]. What I called the gnergy tetrahedron in 1991 can be identified with the body-centered tetrahedron depicted in Figure 43, if gnergy is viewed as being located at the center of the tetrahedron. This leads to an alternative geometric representation of the gnergy tetrahedron as shown in Figure 47. Energy (2) | | | | Gnergy (1) - / \ - / \ Life (5) - / \ / \ Matter (3) Information (4)

Figure 47. The gnergy tetrahedron depicted as a set of four bonds symmetrically distributed around the center occupied by gnergy. Out of the 6 possible edges, two are of special interest – the 2-3 edge representing the energy-matter supplementarity, and the 4-5 edge depicting the information-life supplementarity. These two edges are not in direct contact and are linked only through the gnergy center, thus constituting the two branches of the information-energy complementarity of gnergy.

As mentioned earlier, the gnergy tetrahedron comprises two mutually exclusive or orthogonal aspects -- the diachronic (i.e., through time) and the synchronic (i.e., at the same time). These concepts, first introduced by Saussure, play important roles in modern linguistics [55]. The gnergy tetrahedron appearing in Figure 46 can be viewed as emphasizing the synchronic role of gnergy in maintaining the Universe and life in it as is. In contrast, the gnergy tetrahedron depicted in Figure 47 emphasizes the diachronic aspect, namely, the origin of everything (including life) from Gnergy throughout the history of our Universe. One indirect support for the gnergy model of the origin of the Universe shown in Figures 46 and 47 is provided by the cosmological finding that matter indeed was formed after energy [160] (see the Hawking-Penrose-Guth break in Figure 46). Matter consists mainly of protons and neutrons, collectively called baryons, from Greek word 'barys' S. Ji. 122 meaning heavy, baryons being about 2,000 times heavier than electrons. According to the big bang theory, baryons were produced a few minutes after the big bang in a minute amount relative to that of radiation energy, the ratio between the number of baryons to that of photons being only 6x10-10. This ratio is known as the baryon asymmetry, and the process by which such an asymmetry is produced in our Universe is called baryogenesis, one of the major unsolved problems in contemporary cosmology [160]. The gnergy tetrahedron depicted in Figure 47 may be mapped onto the irreducible triad of Pierce (see Section 3.4) as shown in Figure 48:

Gnergy (Firstness)

Energy-Matter Information-Life (Secondness) (Thirdness)

Figure 48. The mapping of the gnergy tetrahedron onto the Peircean triad. The three-dimensional gnergy tetrahedron was projected onto a two dimensional plane by collapsing the energy-matter and the information-life edges of the gnergy tetrahedron into vertices called “Energy-Matter” and “Information-Life”, respectively.

The logical consistency with which the gnergy tetrahedron and Peircean triad can evidently be combined into one geometric representation as shown in Figure 47 emboldens me to suggest the following two propositions:

“The triadic metaphysics of Peirce represents a projection of (82) the higher dimensional reality of the gnergy tetrahedron onto a lower dimensional formal space.” “The Peircean triadic metaphysics represents the formal (83) aspect of the reality which is more accurately represented by the higher-dimensional metaphysics of gnergy tetrahedron.” If Statements (82) and (83) are valid, it may be concluded that Peircean triadic metaphysics is necessary but not sufficient to represent the ultimate reality and that the gnergy tetrahedron embodying both physics and metaphysics provides the necessary and sufficient conditions for characterizing the ultimate reality.

10.2 A Theory of the Origin of Information based on Peircean Metaphysics

In Section 6.6, we discussed possible physical mechanisms of the origins of biological information and life. In this section, the broader problem of the origin of information in general (including biological and non-biological) is discussed based on S. Ji. 123

Peirce’s metaphysics. As is evident in the following quotations, Peirce made a clear distinction between possibility, Firstness, and actuality, Secondness: "Possibility implies a relation to what exists." [161]

". . . a possibility remains possible when it is not actual” [162]

". . . possibility evolves the actuality" [163]

"In order to represent to our minds the relation between the universe of possibilities and the universe of actual existent facts, if we are going to think of the latter as a surface, we must think of the former as three-dimensional space in which any surface would represent all the facts that might exist in one existential universe." [164]. Feibleman [17] succinctly summarized the essence of Peirce’s' distinction between possibility and actuality as follows: "Not all possibles can exist: actuality is a selection of them." When I read this statement, especially the term "selection", it occurred to me that Peirce's metaphysics might provide a metaphysical foundation for the origin of information in this Universe, since information can be broadly defined as resulting from the selection of a set of objects, events, or entities from a larger set of them. The formalism is very simple. Let us designate the number of all possibilities (or possibles of Peirce) out of which this Universe originated as p, and the number of actual existents (which may be called 'actuals') as a. Then the primordial information associated with (or imparted on) this Universe, to be designated as IC, where C means “cosmological”, may be expressed simply as the binary logarithm of the ratio between these two numbers (assuming for simplicity that all possibles have equal probabilities of being actualized):

IC = log2 (p/a) bits (84)

Although it is almost impossible to measure or determine p and a (and hence IC), the mere fact that we can write down a mathematical expression relating these two quantities to the information content of the Universe may be significant. Equation (84) describes only the informational aspect of the origin of the Universe. The energy aspect of the origin of the Universe appears adequately described by the Big Bang theory in physics. That is, the energy requirement for the selection process implicated in Equation (84) is met by the dissipation of free energy (or entropy production in this case, since the Universe is isolated) attending the expansion of the Universe: Entropy Production p a (85) where the arrow indicates that a actuals have been selected out of p possibles (i.e., p > a). In [38], I came to the conclusion that p might be identified with (all possible) superstrings, and hence a may now be identified with a subset of p reified into elementary particles constituting all the material entities extant in this Universe [38]. S. Ji. 124

(The total number of particles in this Universe has been estimated to be approximately 1080, which is known as the Eddington umber [165, p. 225]. These a actuals are thought to possess sufficient information and energy (i.e., gnergy) to evolve higher-order structures such as galaxies, stars, planets, moons, molecules, the biosphere, and organisms, including humans, under appropriate conditions emergent at specific epochs in the history of the Universe. It is interesting to note that a similar view was recently put forward by a group of cosmologists [166]. The biological information encoded in living systems may be viewed as ultimately derived from the Cosmological Information, IC, through a series of information transductions, similar to the well studied phenomenon of signal transductions occurring in the living cell. If this view of the origin of information is correct, a set of interesting conclusions may be drawn: 1) What happens in this Universe cannot be completely random, including biological evolution. That is, biological evolution may be constrained (or directed) by the cosmological information, IC , encoded in non-living material entities (i.e., abiotic matter). 2) All information associated with this Universe may be continuous with (or traced back to) the origin of the cosmological information at the time of the Big Bang. 3) Possibles, Actuals, and Information may reflect the ontological triad of Peirce:

Firstness (Possibles)

Secondness Thirdness (Actuals, or (Information, or Matter/Energy) Regularities/Laws)

Figure 49. A postulated evolution (or reification) of possibles into actuals and associated information (and laws).

The similarity between Figures 46 and 47 is striking. The similarity may be transformed into an identity simply by equating the gnergy of the gnergy tetrahedron with the possibles of Peircean metaphysics, thus leading to the following conclusions:

“Gnergy is the source of possibles out of which all actuals (86) in the Universe are derived.”

10.3 The Self-Knowing Universe and the Anthropic Cosmological Principle

The postulate that we are living in a universe (called Universum sapiens [38, p. 236]) whose goal (or the final cause of Aristotle) it is to Know Itself arose from the S. Ji. 125 conceptual model of the Universe, the Shillongator, that was constructed on the basis of the principle of self-organization (hence the suffix ‘-ator’) [38, pp. 236-237]. This postulate entails the existence of cosmolanguage and its manifestations as material and mental languages as discussed in Section 8.2, which provides a rationale for the isomorphism between cell and human languages [1-5] (see Sections 3.7 and 8.2). Now this basic postulate may explain other ‘mysterious’ coincidences discovered by physicists in the early decades of the 20th century [165], including the following: 1) The ratio of the electric and gravitational forces between a proton and electron is about 1040. 2) The number of nucleons in the Universe is ~ 1080. 3) The ratio of the action of the Universe to the quantum of action is ~ 10120.

These large dimensionless ratios with the unusual exponents of multiples of 40 is accounted for by Dicke and other cosmologists as the necessary condition for producing carbon-based life forms on this planet [165, 166, 212]. It seems generally accepted among cosmologists that the numerical values of the physical constants, such as c (the speed of light), G (the gravitational constant), h (the Planck constant), e (the electric charge of electrons), mp (the proton mass), and me (the electron mass), must be within a narrow range in order for this Universe to be able to support carbon-based life forms. Deviations of even a few percent in these constants are shown to produce model universes that are devoid of the hydrogen or carbon atoms, without which life as we know it, could not exist. This led Dicke to propose the so-called the Anthropic Cosmological Principle as follows [165]:

“The Universe is the way it is because we exist.” (87)

It should be pointed out that in order to explain the cosmological coincidences mentioned above, it is not necessary that Homo sapiens exists in this Universe. The existence of any carbon-based organisms (e.g., bacteria) other than humanity will do just as well. Another point about (87) is that it seems to assume that the formation of carbon and other elements on this planet inevitably leads to the evolution of Homo sapiens, for which there is no empirical evidence (to the best of my knowledge): The formation of carbon and other elements are necessary but not sufficient condition for Homo sapiens to exist on this planet. For these reasons, I believe that the “Anthropic” part of the Anthropic Cosmological Principle stated in (87) is unnecessary and over-determined and can be better replaced by an adjective related to life forms in general. Thus, I suggest that the more appropriate name for the principle may be the “Organic Cosmological Principle”, which may be stated thus:

“The Universe is the way it is because life exists in it.” (88)

If the postulate of the Self-Knowing Universe is true, then it would follow that the Organic Cosmological Principle is valid, since otherwise no Homo sapiens could exist in this Universe and the Universe cannot know Itself, contrary to the starting premise. If this sequence of reasoning is valid, it would follow that the Self-Knowing Universe S. Ji. 126 postulate IS the true Anthropic Cosmological Principle that was glimpsed by cosmologists five decades ago[165, 166]. The newly formulated Organic Cosmological Principle (OCP) may be elaborated as:

“The numerical values of the physical constants are as (89) they are because they constitute the necessary conditions for there being carbon-based life forms on this planet.”

It is clear that the Organic Cosmological Principle as stated in (89) does not guarantee that the Universe will give rise to Homo sapiens with such a mental capacity. To guarantee the presence of Homo sapiens in this Universe who has the ability to know how the Universe began, it is necessary to make a higher-order postulate that subsumes the Organic Cosmological Principle and provides a sufficient condition for such a self- knowledge. To accomplish this feat, we need a more comprehensive condition subsuming OCP, which I suggest here to be the existence of the cosmolanguage defined in Figure 38:

“In order for this Universe to Know Itself (e.g., Its origin) through the (90) mental activities of Homo sapiens, it is necessary for the Universe to possess the cosmolanguage so that organisms use it to communicate with one another and with their environment.”

We may refer to Statement (90) as the Cosmological Language Principle r cosmological communication principle. Statements (89) and (90) are obviously interconnected, since Homo sapiens is carbon-based organisms. We can therefore combine these two statements into one as follows:

“The numerical values of the physical constants of this Universe (91) are as they are not only because these values constitute the necessary conditions for giving rise to carbon-based organisms on this planet but also because they constitute the sufficient conditions for endowing to some of the organisms in it the capacity to know the origin of the Universe.”

I claim that Statement (91) constitutes the necessary and sufficient condition for the existence of the Self-Knowing Universe, or Universum sapiens, which may be algebraically expressed as follows:

Self-Knowing Universe = Organic Cosmological Principle (92)

+ Cosmolanguage Principle where the equality sign should be read as “ results necessarily from”. S. Ji. 127

Statement (92) may be referred to as the Self-Knowing Cosmological Principle, which comprises two components – the Organic Cosmological Principle which is not anthropic (i.e., dose not depend on the presence or participation of Homo sapience) and Cosmolanguage Principle which is anthropic. If this analysis is right, we are forced to conclude that the traditional version of the Anthropic Cosmological Principle [165, 166, 212] is a misnomer. As evident from above, the postulate of the Self-Knowing Universe (SKU) entails the presence of the cosmolanguage in our Universe which is dual in that it can be reified either as material language or mental language (see Figure 38). I now suggest that the material language comprises at least two major branches – i) particle language, and ii) cell language—just as the mental language comprises natural language, logic, mathematics, visual language, and music. In Section 8.2, we discussed the isomorphism between cell and human languages. It is here postulated that the same set of semiotic/linguistic principles shared by cell and human languages applies to ‘particle language’ as shown in Table 32:

Table 32. A comparison between particle and human languages.

Human Language Particle Language Alphabet (L) Letters Baryons, leptons Lexicon (W) Words Atoms, and molecules Sentences (S) Strings of words 3-Dimesnional aggregates of atoms and molecules through noncovalent bonds Grammar (G) Rules of sentence Laws of interactions among elementary formation particles, including superstrings, quarks, and leptons Phonetics (P) Physiological structures Cosmic gnergons mediating information and processes underlying and energy transfer in space and time, as phonation, audition, and predicted by the Shillongator model of interpretations, etc. the Universe [38, pp. 230-237]. Semantics (M) Meaning of words and Physicochemical processes contributing sentences to realizing the Self-Knowledge of the Universe First Formation of sentences Formation of molecular or atomic Articulation for words aggregates from individual molecules and atoms through noncovalent, weak interactions (e.g., hydrophobic, hydrogen-bond, etc.) Second Formation of words from Formation of atoms and molecules Articulation letters through strong covalent and gluonic interactions S. Ji. 128

Third Formation of texts from Formation of large molecular and atomic Articulation sentences aggregates through gravitational interactions (?)

Eq. (92) and Table 32 may be combined into one statement:

“Fundamental particles in the Universe and their interactions (93) obey a set of rules to produce complex structures and processes that are necessary and sufficient to evolve Homo sapiens with the capacity to uncover the origin of the Universe.”

Statement (93) can be viewed as constituting the cosmolanguage predicted in 8.2, since it contains both particle and mental languages and their semantics. The probability, P(U), of observing the Self-Knowing Universe may be expressed th as the product of the probabilities, pi(v), of the i fundamental physical constant having a specific numerical value, v, where i = 1, 2, 3, . . . , n, where n is the total number of the critical physical constants whose numerical values determine the physical properties of the Universe. The current cosmological evidence seems to suggest that there are at most 6 such critical physical constants: c (speed of light), h (Planck constant), G (gravitational constant), e (electronic charge), me (electron mass), and mn (neutron mass) [165, 166]. The probability of the existence of the Self-Knowing Universe, then, may be expressed as: n P(U) = Π pi(v) (94) i = 1

It is assumed that the critical physical ‘constants’ mentioned above can assume (in different Universes) numerical values that are different from the ones assumed by them in this Universe. In other words, Eq. (94) assumes that there are a large number of Universes, other than our own, that are characterized by different combinations of the numerical values of the fundamental physical constants. The physical meaning of P(U) is that our universe is one of the 1/P(U) possible Universes out of which it has been selected (or arose). Hence we can equate 1/P(U) with p/a in Eq. (84), thus leading to the conclusion that the information content, IC, of our Universe at the time of the Big Bang is:

IC = log2 [1/P(U)] bits = - log2 P(U) bits (95)

10.4 Semiotics as the Theory of Everything (TOE)

Physicists have their TOE in the form of superstring theories which may fulfill their dream of unifying the four fundamental (i.e., gravitational, electromagnetic, weak, and strong) forces of nature [167-170]. Even if their optimism is realized in the near future, it is not clear how superstring theories (or their equivalents) will be able to provide satisfactory explanations for everything in the Universe, such as life and S. Ji. 129 consciousness, mathematics and linguistics, and literature and art. The semiotics of Peirce, appropriately updated by taking into account the new developments in systems theory, cybernetics [159, 171], molecular and cell biology [35, 38, 57], and humanities [52-54, 141], may provide a true Theory of Everything (TOE) that will account for everything in this Universe, including superstring theories themselves [167-170] and category theories [172-175], the most abstract of mathematical systems yet devised by the human mind. The logical path that has led me to this broad conclusion can be schematically represented as follows: Points (Set Theory) --> Objects (Category Theories) --> Signs (Semiotics) (96) The main idea behind Scheme (96) is that, just as mathematicians developed, in the mid-1990's, category theories by replacing simple points with more complex mathematical entities known as 'objects' of a category, so perhaps semioticians could generalize category theories by replacing their objects with Peircean signs (appropriately updated as indicated above). Such a project seems eminently logical and reasonable, because points and objects are clearly 'signs' as defined by Peirce: "A sign, . . . , is something which stands to somebody for something in some respect or capacity" (see Section 2.2)[12, p. 99;176]. But, before I elaborate on this path below (see Table 33), let me first discuss a more global piece of evidence for my claim. A casual perusal of the field of cognitive maps suggested to me that there is a sufficient amount of evidence to support the notion that all human knowledge can be represented visually using graphs, which are iconic signs according the sign classification scheme of Peirce (see Section 7.3 and Table 18). A graph can be defined as a 2-tuple, Graph = G (V, E) (97) where V is a set of vertices (also called nodes, or points) and E is a set of edges (also called links, arcs, or arrows) connecting two or more nodes. The basic unit of a graph can be considered to be a triad consisting of two vertices, A and B, which are the elements of V, and one edge, f, an element of E:

f A B (98)

For convenience, and to highlight the importance of the unit structure of a graph, it may be convenient to refer to it as the “graphon". The graphon so defined may be identical with the semion recently defined by Gudwin as the unit of semiosis [209]. Now it is truly remarkable that the graphon defined in (98) can be found to appear in practically every field of human inquiry, including: 1) Set theory, 2) Topology, 3) Category theory [172-175], 4) Theoretical computer science (e.g., labeled deductive systems [177, 199]), 5) Physics (e.g., vibrating superstrings --> fermions + bosons [167-170]), S. Ji. 130

6) Chemistry (e.g., chemical reactions), 7) Biology (e.g., metabolic pathways; signal transduction pathways [199], genetic network), 8) Psychology (e.g., fuzzy neural networks [178, 179]), 9) Behavioral science [180] 10) Linguistics [181], 11) Text analysis [182], and 12) Social sciences [183]. Evidently, this list covers most, if not all, of the major disciplines in human knowledge, and all of them involve the graphon, Process (98), in one way or another. Therefore, if the graphon can serve as a diagrammatic representation of Peircean signs, then it may be logical to conclude that semiotics can serve as a Theory of Everything (TOE). I claim that the graphon depicted in (98) is a composite sign consisting of the following three categories of elementary signs: 1) A and B representing the potentialities (Firstness), 2) A B representing the actualization (Secondness) of a change, interaction, or process, and 3) The symbol f representing the rules, laws, or habits (Thirdness)leading to, causing, or enabling the A B process. If this conjecture is valid, the semiotics and the associated metaphysics of Peirce will be applicable to (and hence will provide a general theoretical framework for) Everything in the Universe as we know it. Returning to the logical path, Scheme (96), Table 33 below provides a more detailed description of the progressive generalizations of the set theory to category theories on the one hand and the latter to semiotics on the other. By the expression, "generalizing A to B", I mean i) that B contains A as a limiting case, and ii) B exhibits properties not found in A. We may refer to i) as "subsethood" and ii) as "emergence". Table 33 compares the set theory, category theories, and semiotics, using graphics- theoretical terms.

Table 33. Three levels of description of entities in the Universe

Theory Nodes Edges Domain of Application

Set Theory Points Mappings Mathematics (e.g., Euclidean space, Banach space, Hilbert space)

Category Theory Objects Morphisms Mathematics, Physics, Logics S. Ji. 131

[173] (the Eilenberg- MacLane space (?))

Semiotics Signs Relations Mathematics, Physical Sciences, Literature, Art, etc. (the ‘Peirce space’ (?))

The concept of 'space' plays a fundamental role in mathematics, as evident in the existence of many named spaces in mathematics and physics, some of which are cited in Table 33. I think the concept of "sign space" will be similarly useful in semiotic discourses. We may define the "sign space" as a collection of Peircean signs with possibilities of rule- or law-obeying interactions among them. We may refer to such a space as the "Peirce space", in analogy to the Banach space or the Hilbert space. Thus defined, it is easy to envision that the Peirce space will be populated by all the regularities of signs characterizing human discourses, including Peirce's own discussions on signs, Nauta's signal-sign-symbol triad [159, 171], Spink's sign pyramids [52-54], Frege's sign triangle [184], Lobanov's 'principal sign situations' [184], and the 'body-centered sign tetrahedron' discussed in Sections 8.4 and 10.1. In other words, all human discourses, from mathematics to physics to chemistry to biology to linguistics to psychoanalysis to philosophy to literature and art, can be represented as networks of appropriate signs in the "Peirce space", which thus may be viewed as a geometric representation of the semiotics as a TOE. It may be further postulated that semiotics is an irreducibly triadic science whose components can be identified with three distinct branches of sciences, each centered on studying energy/matter, information, or the energy/information duality, i.e., gnergy. The irreducibly triadic nature of semiotics is depicted by the large square bracket in Figure 49 as before, and the suggested contents of the three branches of sciences are given in Table 33.

Gnergy Science

Semiotics =

Energy Information Science Science

Figure 49. Semiotics as the irreducible triad of three aspects of sciences. Each branch is focused on the study of the transformation of energy/matter, of information, or of the hypothetical entity, gnergy (defined as a complementary union of energy/matter and information/life). S. Ji. 132

The "irreducibility" in Figure 49 means that semiotics cannot be reduced to any one or a pair of the three branches and that semiotics can only be characterized as a combination of all these three branches, although certain branches may predominate over others, depending on the point of emphasis dictated by the method of inquiries employed. Based on Figure 49, it may be suggested that the version of semiotics that Peirce developed in his life time was mainly focused on the 'informational aspect' of the semiotics triad, although he was probably aware of the energy/matter aspect of semiosis as well. It may further be speculated that the energy/matter aspect of semiotics has been forced upon the scientific community with the rapid development of molecular and cell biology and brain sciences in the 20th century.

Table 34. Semiotics as the theory of everything encompasses three main branches of sciences. As a consequence of the irreducibly triadic nature of semiotics, the boundaries among the three branches of sciences cannot be sharply demarcated but are rather vague or fuzzy in the sense of Zadeh and Kosko [178], the degree of fuzziness being more or less proportional to the complexity of the system under consideration.

Energy Science Information Science Gnergy Science Primitive Energy/Matter Information Gnergy Concept Philosophy Superstring Theories (?) Logic Cosmology Classical Physics Mathematics Biology Quantum Physics Examples Computer Science Cognitive Sciences Cosmology Information Theories Psychology Chemistry Linguistics Sociology History Economics (?)

The contents of Table 34 comprise a good part of the human knowledge extant at the beginning of the 21st century. That human knowledge, viewed as both human and natural phenomena, should divide into three distinct branches may not be too surprising if the 'irreducibly triadic' natures of Peircean phenomenology and ontology are taken into account. One of the most important contributions that the 20th-century biology has made to human knowledge is the discovery that molecules can act as signs (e.g., DNA, hormones, etc.), which fact was unknown to Peirce and his predecessors. With Watson and Crick's discovery of DNA as a molecular sign carrying genetic information, it may be claimed that the traditional semiotics of Poinsot and Peirce bifurcated into two branches -- (i) macrosemiotics studying signs of macroscopic sizes (i.e., pictures, words, sentences, texts), and (ii) microsemiotics concerned with signs of molecular dimensions [4,5]. The unexpected finding [1-5] that cells use a language of their own that obeys a set of linguistic (or, more generally, semiotic) principles in common with human language (see Table 3) supports the thesis that semiotic principles are not confined to S. Ji. 133 human communication but also extend to communication processes on the molecular level: Macro- and micro-semiotics obey the same set of semiotic principles, just as macrophysics and micro-physics obey the same set of thermodynamic laws. The concept of energy (the ability of a system to do work) played an important role in physics in the 20th century, at both the macro- and the microscales. Similarly, it may turn out that the concept of signs as defined by Peirce in terms of his triad of trichotomies (iconic sign, indexical sign, symbolic sign; representamen, object, interpretant; Firstness, Secondness, and Thirdness; see Section 7.4) may play a key role in biology and related disciplines in the 21st century, at both the macro- (e.g., evolution, psychology) and microscales (e.g., control of gene expression). The latter conjecture seems to be supported by the following observations:

(1) The concept of gnergy has been claimed to integrate all scientific and philosophical systems within a coherent framework known as complementarism, including topics ranging from the origin of life to molecular and cell biology, and from cosmology to metaphysics [11,30, 38]. (2) Gnergons, discrete units of gnergy, are postulated to provide the ultimate driving force for all self-organizing processes in the Universe, such as the Big Bang, the origin of life, evolution, and communication in living systems [38]. Therefore, signs may be considered as constituting a subset of gnergons (see Appendix IX). Since gnergy is a complementary union of information and energy, the complementary relation shown in Figure 51 results:

Semiotics

Energy Science Information Science

Figure 51. Energy and information sciences viewed (or prescinded) as complementary aspects of semiotics. Energy sciences include thermodynamics, Newtonian mechanics, relativity theories, quantum mechanics, electrodynamics, statistical mechanics, chemical kinetics, and information sciences include linguistics, logic, mathematics, information theory, both crisp and fuzzy, and computer science (see Table 33).

If Figure 51 is right, semiotics will be able to provide an overarching theoretical framework to integrate most, if not all, of the scientific and philosophical disciplines known to humans in the 21st century. It should be pointed out that Figure 51 differs from the architectonic theory of Peirce in two respects: (i) It is much simpler than Peirce's scheme of organizing human knowledge, and (ii) it is based on the logic inherent in the principle of energy-information complementarity. It should be noted that the content of Figure 51 is identical with that of Figure 49, since “semiotics” as a Peircean sign has a dual meaning – one that is triadic as shown in Figure 49 and the other that is monadic as shown in Figure 51. S. Ji. 134

10.5 Iconic Model of Reality

Peirce pointed out that, although traditionally symbolic signs have been used overwhelmingly in human thought (since the invention of written languages), iconic signs provide certain advantages over symbolic ones in human reasoning. Reasoning based on a combined use of both symbolic and iconic signs is known as the multimodal or heterogeneous reasoning [47]. The purpose of this Section is to describe an iconic model of reality, in contrast to the models of reality discussed during the past two and a half millennia based primarily on symbolic signs (e.g., verbal discourses, written sentences and texts) that have been espoused by philosophers and quantum theorists. The complementarian logic (see Section 3.3 and Appendixes I and VIII) was formulated in the mid-1990's by generalizing Bohr's complementarity concept in such a way that it could be applied to fields beyond physics, including biology, psychology, brain physiology, philosophy, and religion [11, 30]. Recently, H. Atmanspacher made a similar attempt and generalized algebraic quantum theory in the form of what is known as the 'weak quantum theory (WQT)' [46]. The logic behind WQT is expressed in a mathematical language, whereas the logic of complementarism has been expressed in terms of a multimodal or heterogeneous reasoning [11, 30] that utilizes not only the written language (symbolic signs) but also diagrams and tables (iconic signs). It will be convenient to define two 'operators' to be referred to as the "complementary cut", Cc, and the “complementary splicing", Cs, as shown in Equations (98) and (99): Cc[C] = A^B (98) Cs[A^B] = C (99) where X[Y] indicates that operator X acts on operand Y, and the symbol, W^Z, indicates the complementary relation between W and Z generated by the complementary cut. Eq. (98) indicates that C produces the complementary pair, A and B, as a result of the complementary cut, Cc, acting on a third entity, C. Eq. (99) states the reverse, namely, that A and B can be spliced together to generate C. Since (99) is the inverse of (98), we may designate Cs as Cc-1. Using the concepts of the complementary cut and splicing defined in Eqs. (98) and (99), I postulate here that

"Reality is the totality of what exists in the Universe, (100) both material and nonmaterial, interconnected with one another through cuts and splicings that obey the complementarian logic."

Postulate (100) can be iconically represented (in the sense described in [47]) as shown in Figure 52 and Table 33. Figure 52 provides a convenient theoretical framework to integrate the various ideas and concepts invoked to explain/define reality in the history of philosophy and physics (see Table 33). S. Ji. 135

In conclusion, the iconic model of reality presented in Figure 52 should be regarded as a theoretical "Net" cast in the sea of Reality, and the contents of Table 33 represent what has been caught so far with the net. It is hoped that most, if not all, of the major "fish" swimming in the sea of Reality has been captured in Table 33. If there is any defect in the net, it is hoped that they will be repaired (or the whole net modified to improve the catch), in the hands of philosophers and scientists in the coming decades.

Splicing (3->1) ______\ Reality (R) | / | | | Cut 1 | ______|______| | | | | | | | | | Rationality (A) Irrationality (B) | | | | Cut 2 | ______|______| | | | | | | Matter (C) Mind (D) | | | | | Cut 3a | Cut 3b | ______|______|______| | | | | | | | | | |___ Observed Observing Observed Observing Matter Matter Mind Mind (E) (F) (G) (H)

Figure 52. An iconic model of reality. The ultimate reality is postulated to be the sum total of the nodes in this figure that are interconnected by cuts and splicings obeying the complementarian logic. Cuts produce diversity, while splicings produce unity. The symbol, Splicing (3--> R) indicates that the results of the third cut can be spliced together to regenerate Reality (epistemologically) to a varying degrees of correspondence.

Table 33. A suggested identification of the nodes and the edges of the iconic model of reality (shown in Figure 52) with traditional philosophical and scientific ideas in the history of human knowledge. S. Ji. 136

Notice that the terms appearing in this table for the first time are indicated by quotation marks. The second column lists relevant ideas from philosophy, especially from Peircean triadic metaphysics/ontology, and the third column lists those from quantum physics.

Iconic Model Philosophy Physics

Cut 1 the Pauli cut1 ---

Cut 2 the Cartesian cut ---

Cut 3a --- the Heisenberg cut

Cut 2b --- the James cut2

Splicing (3-->R) Peircean Thirdness ---

R Ultimate Reality --- Irreducible Triad of Peirce

A Gnergy --- Firstness

B Jung’s Archetypes ---

C Secondness Matter/Energy

D Mind --- Thirdness

E Secondness Object

F Secondness Measuring Instruments

G Observed Self/Mind --- Consciousness (Thirdness)

H Subject (?) Observer S. Ji. 137

1Pauli believed that reality embodies both rationality and irrationality and that the latter corresponded to Jung's group unconsciousness [185]. 2I named this cut as indicated to honor William James who first used the term "complementary" to describe human consciousness in terms of two mutually exclusive consciousnesses, first observed in patients with hysterical anesthesia [206]. So, it may be stated that the concept of complementarity in the traditional quantum mechanical [19-22], the weak quantum mechanical [46], and the complementarian logical [11, 30] senses all began with William James [206].

10.6 Life as an Intrinsic Aspect of the Universe

There is a large body of evidence, both physical and metaphysical, that supports the notion that information and energy are complementary. For example, in biology, the sequence-specific conformational strains embedded in biopolymers such as proteins and DNA known as conformons (that are postulated to drive all goal-directed molecular motions in living cells) carry both genetic information and mechanical energy [34] (see Section 5). Just as physicists have not been able to measure the particle and wave properties of light simultaneously (to the best of my knowledge), biologists have not been able to measure the energetic and informational aspects of conformons simultaneously. If information and energy are indeed complementary in the sense defined by N. Bohr [190] (also see Appendix I), there must exist a third entity for which information and energy are complementary aspects, just as there exists light of which waves and particles are its complementary aspects. In philosophy, Aristotle’s hylomorphism(as a union of form and matter) [27], Spinoza’s Substance (as a union of Extension and Thought) [28, 188], and Merleau-Ponty’s Flesh (as a union of Mind and Body) [29] may all be viewed as embodying complementarity relations [11]. As briefly discussed in Section 3.1, the term ‘gnergy’ was coined in the mid- 1980’s to represent the complementary union of information (gn-) and energy (-ergy) [38], and a triangle was used to visualize the triadic relation:

Gnergy

Energy Information

Figure 53. A diagrammatic representation of the complementarity between information and energy. S. Ji. 138

Figure 54. A theoretical model of the Universe first proposed at the International Seminar on the Living State III, held at the North-Eastern Hill University, Shillong, India, December 13-19, 1986. The model postulates that the Universe began with Gnergy, a complementary union of information and energy, and various aspects of this ultimate driving force revealed themselves as the Universe expanded and aged, giving rise to a series of six breaks as indicated in rectangles numbered 1 through 6 [38, pp. 230- 237]. For detailed explanations of each break, readers are referred to the original publication. For the purpose of this Section, it is sufficient to note that it is possible to account for the evolution of the Universe, including the emergence of Homo sapience and culture, on the basis of the Gnergy concept.

The gnergy concept arose in the context of molecular biology. In the late 1980’s, I extended the gnergy concept to physics by applying it to the origin of the Universe [38, pp. 154-163, 230-237] (see Figure 54 below). In the process, I was led to 2 incorporate the result of special relativity, namely, E = mc , were E is energy, m is the rest mass, and c is the speed of light. This meant that I had to replace the Energy vertex in Figure 53 with a line connecting Energy and Matter, transforming a 2-dimensional triangle, the simplex of the 2-dimensional space, into a tetrahedron, the simplex of the 3- dimensional space. Since I believed in the ‘symmetry’ between information and energy, if energy has its matter, I thought information should have its own matter analogue as well. After some deliberation, I came to conclude that the matter analogue of information might be life itself. So, in analogy to the ‘wave-particle duality’ in quantum physics, I began to use the S. Ji. 139 term ‘energy-matter-information-life tetrahedrality’ [38, p. 234] and depicted it as a tetrahedron called the “gnergy tetrahedron” [38, see Figure 1.A5] which is shown below as a body-centered tetrahedron:

Energy (E) | | | Gnergy (G) - / \ Life (L) - / \ / \ / \ Information (I) Matter (M)

Figure 55. The tetrahedral topology of gnergy. The E/M and I/L edges are the two complementary aspects of G; The vertices E and M are supplementary to each other, as are the I and L vertices. For the definitions of complementarity and supplementarity, see Appendix I.

There are two distinct principles embedded in Figure 55. To bring these out clearly, I will refer to the supplementary union of energy and matter as “matter-energy” or “mattergy”; the supplementary union of information and life as “life-information”, “linformation”, or simply “mind”. Using these neologisms, I claim that Figure 55 embodies two fundamental principles that Bohr spoke of in [190] (see Appendix I) – the complementarity principle depicted in Figure 56, and the supplementarity principles in Figures 57 and 58:

Gnergy

Energy/Matter Information/Life (‘Mattergy’) (‘Linformation” or ‘Mind’)

Figure 56. A diagrammatic representation of the complementarity (symbolized by a triangle) between Matter/Energy and Information/Life (also called ‘Linformation’ or ‘Liformation’) .

S. Ji. 140

Mattergy (Matter/Energy) | | | Matter______|______Energy

Figure 57. A diagrammatic representation of the supplementarity, (symbolized by __|__ ) between Matter and Energy.

Mind (‘Liformation’) | | | Life ______|______Information

Figure 58. A diagrammatic representation of the supplementarity (symbolized by __|__ ) between information and life.

If the above analysis is right, one surprising conclusion is that life can be viewed as a highly condensed form of information, just as physicists view matter as a highly condensed form of energy. The theory behind the energy-mater supplementarity is Einstein’s special relativity which is built upon the foundations of Newtonian mechanics and Maxwell’s electromagnetism, plus the empirical fact of the constancy of the speed of light [210, 211]. Analogously, there may be a new theory to be constructed to account for the information-life supplementarity. I suggest that the sought-after theory may be identified with a theory of molecular systems constructed on the basis of the laws of Newtonian dynamics, quantum mechanics, statistical mechanics, thermodynamics and information theory, plus the empirical fact of biological evolution which has endowed living systems with genetic information that guides/constrains the behaviors of their molecular components in the direction of accomplishing gene-directed goals. One such theory, referred to as the molecular information theory, was recently formulated [35]. It is envisioned that the molecular information theory will represent the dynamics of living systems as fuzzy trajectories in the phase space [213] that also accommodates the principle of rule-governed creativity (see Section 3.16).

10.7 Semiotics of the Universe

This contribution began with the title, Semiotics of Life, and yet its closing Section is entitled Semiotics of the Universe. The reason for this titular transition is not an S. Ji. 141 accident but related to the fact that life is an intrinsic aspect of the Universe we live in, as discussed in Section 10.6, and hence it is natural to conclude that semiotics of life cannot be described without addressing the semiotics of the Universe Itself. The topological object, body-centered tetrahedron (BCT), originated from extending the concept of gnergy from biology to cosmology, i.e., to the problem of the origin of the Universe. It was then further extended to modeling the living cell (see Figure 2 on p. 202 in [186]), the human body [ 38, p. 144], the human mind (see Figure 40 in Section 8.5), and the Peircean sign (see Figure 41 in Section 8.4). Although the nature of nodes and edges in these applications of BCT are different (as summarized in Table 36), all these applications reflect the topological properties of the body-centered tetrahedron (BCT) (see Figure 45). It should be recalled that BCT was given the name, the Tarragonator, because it belongs to the set of topological objects exhibiting the so- called Möbius relations (see Table 2 in Section 3.5). Therefore, it may be justified to refer to BCT as a cosmic code, since it may reveal the topological structure of our Universe. It is interesting to note that H. Pagels used the same phrase, ‘cosmic code’, in 1982 to refer to quantum physics [187]. But BTC, as a cosmic code, symbolizes not only quantum physics (in the form of information-energy complementarity) but also the theories of special relativity (in the form of energy-matter supplementarity), biological evolution (in the form of information-life supplementarity), and the topology of the Möbius band (see Section 3.5). Additionally, BCT is a cosmic code, because it has the character of a cosmic formula, whose meaning or output depends on the information that is inputted into the five nodes. One such example is shown in Figure 59, which reveals (i) the physical principles underlying the physics of our Universe, namely, the principle of the information-energy complementarity and the two supplementarity principles, one involving energy (E) and matter (M) and the other involving information (I) and life (L), and (ii) the topological principle of Möbius relations, which states that

“The Universe is E, M, I, or L, when viewed locally, (101) and E, M, I, and L (i.e., G) when viewed globally.” Finally, it should be noted that Figure 59 symbolizes another set of interesting features of our Universe: 1) The square is divided into two regions – the dark region symbolizing the unknowable and the light circular region symbolizing the knowable. We may refer to the division between the unknowable and the knowable as the Homo sapiens cut, because it is determined by the innate intellectual, cognitive, and emotional capacities of our species, just as the size of the event horizon is determined by the finitude of the speed of light. 2) The circle divides into two regions -- the region outside the tetrahedron symbolizing the nonrational or the irrational and the region enclosed within the tetrahedron symbolizing the rational. Thus the surface of BCT corresponds to the Pauli cut discussed in Section 10.5. 3) The tetrahedron comprises several subspaces, each symbolizing various disciplines in philosophy and natural sciences: a) The GIL plane may be identified with the semiotics of Peirce whose main concern was with signs (here interpreted as the carriers of I; see Section S. Ji. 142

9.3) and consciousness (which presupposes L). He obviously did not discuss G, gnergy, but his conception of Firstness may be closely related towhat is here called G, and his Secondness and Thirdness, to I and L, respectively. b) The EMI plane encompasses physics and chemistry, which concentrate on abiotic systems (thus excluding L) and exclude any explicitly metaphysical concepts such as G. c) The contemporary biology, including molecular biology, may be best represented by the surface of the tetrahedron, which, like physics, exclude G but includes E (e.g., the importance of ATP), M (e.g., molecular structures), I (e.g., genetic information of DNA), and L (e.g., biological evolution). In contrast, the theory of life (or semiotics of life) advanced here asserts that biology needs not only E, M, I and L, but also G, the ultimate mover (or driving force) for all self-organizing processes in this Universe, including the origin of life and its moment-to-moment maintenance on this planet. d) The contemporary cosmology may be represented by the GEM triangle, which is distinct from the EMI triangle symbolizing physics. The main difference between these two disciplines is the strong metaphysical component, i.e., G, explicitly invoked in cosmology, as exemplified by the anthropic principle [165, 166, 212]entailed by the need to account for the many mysterious numerical coincidences that have been found in physical constants over the past half a century (see Section 10.3). e) Religion is excluded from BCT because religious considerations or propositions belong to the nonrational and/or the unknowable, the two regions lying outside of BCT. Therefore G is distinct from God or its equivalents of the major religions of the world.

On the basis of this interpretation, it may be concluded that the new biology as conceived and envisioned in this article, subsumes not only physics and chemistry but also Peirce’s semiotics and philosophy in general, which characteristics we would associate with a theory of everything. That is, it may be concluded that the theory of life proposed here may be viewed as a true theory of everything. S. Ji. 143

Figure 59. The gnergy principle of the Universe depicted as a body-centered tetrahedron. G = Gnergy, E = Energy, M = Matter, I = Information, and L = Life. The model of the Universe based on the gnergy principle is known as the Shillongator (see Figure 53). Table 36. The body-centered tetrahedron as the iconic language of the Universe and its constituents. The five nodes are numbered as in Figure 44.

The Nodes of the Body-Centered Tetrahedron Systems 1 2 3 4 5

1. Universe Gnergy Energy Matter Information Life

2. Cell Environment Biochemicals Proteins RNA DNA

3. Body Motion Nervous Circulatory Endocrine Immune System System System System System

4. Mind Biochemicals DNA Cells Brain Mind S. Ji. 144

5. Signs Gnergy Sign Processor Representamen Object Interpretant (Firstness) (Secondness) (Thirdness)

In Section 8.2, we discussed the isomorphism between cell and human languages and were led to suggest that there exists a third language in the Universe called the ‘cosmolanguage’ that acts as the source of both cell and human languages, but we were unable to elaborate on the nature of the cosmolanguage. This gap may now be filled with the content of Table 36, which may be viewed as representing a language with 5 nodes acting as words and the topology of BCT as the grammar, embodying the two linguistic relations, referred to by Saussure as syntagmatic and paradigmatic relations [55]. The syntagmatic relation is exhibited by the elements of the rows, the paradigmatic relation is manifest by the elements of the columns. Each of the 5 rows can be interpreted as a cosmological sentence (or ‘cosmosentence’) of the following form: X can be theoretically represented as a BCT consisting of (102) the nodes 1, 2, 3, 4, and 5. The ‘cosmosentence’ in (102) shows syntagmatic relations among X, 1, 2, 3, 4, and 5 and paradigmatic relations among X, Universe, Cell, Body, Mind, and Sign. The cosmolanguage defined in Table 36 is rooted in the topology of BCT, which is also a cosmic code, or a sign for the Universe. To relate all these diverse elements coherently, we need to resort once again to the definition of signs given by Peirce (see Section 7.3). It should e recalled that Peirce defined a sign as an irreducible triad of representamen (or sign vehicle), object, and interpretant (see Figure 1). If we apply this definition to the Tarragonator viewed as the sign of the Universe, the following irreducible (as symbolized by the square brackets) triad results, consisting of BCT as Representamen, the Universe as its Object, and the theory of cosmolanguage as its Interpretant: __ __

Body-Centered Tetrahedron

The Tarragonator =

The Universe Cosmolanguage __ __

Figure 60. The Tarragonator as the Sign of the Universe. The Tarragonator is characterized by the following key features: 1) The Tarragonator, constructed on the base of my theoretical investigations going back to the early 1970’s represents the most comprehensive theoretical model of the Universe yet formulated (to the best of my S. Ji. 145

knowledge). 2) The Tarragonator is related to the earlier version of the model of the Universe, the Shillongator, by having an additional element, the cosmolanguage (defined by Table 36): The Tarragonatgor = The Shillongator + Cosmolanaguage (103)

3) The cosmolaguage can be interpreted as the general law or the cosmological code that connects the topology of body-centered tetrahedron to the extant Universe.

11 Conclusions

Over the past three decades, I have been investigating the phenomenon of life on the molecular, cellular, and multicellular organismic levels as a chemist-turned- theoretical cell biologist. The method of reasoning employed has been heterogeneous or multimodal, utilizing all possible modes of being of signs – iconic (e.g., diagrams, tables, mathematical equations), indexical (e.g., visual feelings), and symbolic (e.g., words, sentences, texts). Most of the key results of my theoretical investigations since the early 1970’s have been summarized here. In addition, this article includes several new developments reported here for the first time: (1) Life and information are supplementary, just as energy and matter are supplementary through Einstein’s equation, E = mc2. That is, life can be viewed as resulting from a highly condensed state of information, as exemplified by informational macromolecules such as DNA, RNA and proteins that carry out their gene-instructed molecular processes inside the cell, utilizing the free energy released from the chemical reactions that they catalyze. The cell is alive perhaps because it has an information density which is beyond some critical threshold essential for exhibiting living properties. (2) The body-centered tetrahedron can be viewed as the cosmic code or a sign for our Universe. It stands for our Universe iconically, indexically, and symbolically, mediated by a cosmic language (called the ‘Cosmolanguage’) whose material and mental manifestations having been identifiable with cell language (and associated particle languages) and human language, respectively. (3) The semiotic theory of the Universe reported here is built on earlier works (embodied in the Shillongator described in Section 10.1) and on (1) and (2) above and is named the Tarragonator (see Figure 59) to indicate the fact that the beginning of its formulation can be traced to the lectures that I gave at the Rovira i Virgili University in Tarragona in 2003. (4) The Tarragonator is a theory of everything (TOE) that encompasses superstring theory, quantum mechanics, special relativity theory, theory of biological evolution, the conformon theory, the cell language theory, and the anthropic principle--all integrated into a coherent whole based on a combination of Peircean semiotics and the topological principle of the Möbius band (see Section 3.5). (5) If the Tarragonator can be proven to be valid by future investigations, the mystery of life will finally have been solved. That is, if the Tarragonator turns out to be S. Ji. 146 right in essence, it may serve as a firm foundation for the semiotics of life that will self- organize to reveal the true nature of our Universe and ourselves in It.

Acknowledgement.

I want to thank my wife, Jaehyun Lee, for her help, both technical and intellectual, in preparing this manuscript as well as for her understanding and perseverance. My thanks also go to the members of the Department of Pharmacology and Toxicology at the Ernest Mario School of Pharmacy at Rutgers University for their understanding and tolerance for my somewhat unusual research interests and activities, which often got in the way of developing mutually beneficial collaborations in teaching and research. I would also like to thank the members of the NECSI mailing list managed by Dr. Yaneer Bar-Yam for stimulating discussions and for the helpful inputs received over the past several years. Finally, I owe special thanks to Carlos Martin-Vide for his kind invitation to Tarragona in 2003 and for his far-sighted suggested title, “Semiotics of Life”, for my lectures there, without both of which this manuscript and the Tarragonator it describes would not have been born.

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Appendices

Appendix I Complementarity vs. Supplementarity

Mailing List [email protected] Message #4240 From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: Complementary vs. supplementary models of X

Date: Thu, 10 Oct 2002 01:47:51 -0400

To: complex-science

To contribute to recent discussions on modeling human society or mind, let me suggest that it may be useful to distinguish two classes of models of anything, X, be it human society, mind, the brain, the cell, enzymes, atoms or even the whole Universe -- the complementary and supplementary models. We may designate these two classes of models as Mc(X) and Ms(X), respectively. As will be explained below, the ideas of COMPLEMENTARITY and SUPPLEMENTARITY were first clearly enunciated by Niels Bohr in 1958 [1]. But before I quote the relevant paragraph from [1], let me explain the major difference between these two classes of models: Supplementary models are ADDITIVE while complementary models are MUTUALLY EXCLUSIVE. We can represent these ideas algebraically as follows:

n Xj = ∑ Ms(X)i, j (1) i = 1 where Xj is the jth complementary aspect of X, Ms(X)_i,j is the ith supplementary model of X viewed or measured under the jth presupposition or grounding. n is the number of different supplementary models, all measured under the jth grounding. Using the same symbols defined above, we may represent the relation between X, the complete object, and its complementary models as follows:

X = Xj but not Xk under presupposition j or Xk but not Xj under presupposition k, etc. (2) where j and k are nonidentical indexes independently ranging form 1 to m, the maximum number of complementary models of X; Xj and Xk are the complementary models of X measured under the jth and kth groundings, respectively. We may abbreviate Eq. (2) as S. Ji. 158 follows which in effect defines the meaning of a new symbol,Γ, from "complementarity":

m X = Γ Mc(X)j/=k (3) j/=k = 1 where j/=k reads "j not equal to k", both indexes independently ranging from 1 to m. Equations (2) and (3) simply mean that the complete description of X requires a set of m complementary models (each having a set of n supplementary models obeying Eq. (1)). In an illuminating article [2], J. F. Rychlak suggested a list of four complementary models that are needed to accommodate all of the major theories in contemporary psychology: 1. Physikos = Models of mind grounded in fundamental physicochemical processes. 2. Bios = Models of mind based on the fundamental difference between life and nonlife. 3. Socius = Models of mind emphasizing group relations and cultural influences 4. Logos = Models of mind centered around cognitive organizations (called meaning), intelligence, prediction, construing, and other mental acts. Applying Eq. (2) to Rychlak's suggestion, we may say that X = Mind, and j and k = 1, 2, 3, or 4, as defined above. The only drawback that I see in of Rychlak's otherwise exhaustive scheme is that he did not consider the multiplicity of supplementary models possible under a given ground or presupposition. That is, Rychlak's scheme is based only on Eq. (2), while a complete description of X requires both Equations (1) and (2). Finally, let me quote Bohr [1] who defined the important distinction between complementarity and supplementarity as follows: " . . . Within the scope of classical physics, all characteristic properties of a given object can in principle be ascertained by a single experimental arrangement, although in practice various arrangement are often convenient for the study of different aspects of the phenomenon. In fact, data obtained in such a way simply supplement each other and can be combined into a consistent picture of the behavior of the object under investigation. In quantum mechanics, however, evidence about atomic objects obtained by different experimental arrangements exhibits a novel kind of complementary relationship. Indeed, it must be recognized that such evidence which appears contradictory when combination into a single picture is attempted, exhausts all conceivable knowledge about the object. Far from restricting our efforts to put questions to nature in the form of experiments, the notion of complementarity simply characterizes the answers we can receive by such inquiry, whenever the S. Ji. 159

interaction between the measuring instruments and the objects forms an integral part of the phenomenon. . . . " One important conclusion arising from the complementarity approach described by Rychlack is that there is no hierarchical relation among complementary models, Mc(P), Mc(B), Mc(S), and Mc(L). They are all on an equal footing, meaning that none of them are causal to any others. All these complementary models have equal rights to be primary, just as waves and particles can equally well be regarded as the primary aspect of light. We may call such an idea the "causal democracy" or "ontological democracy." If this approach is right, we may have to learn to live with multiplicity of complementary models of the brain/mind and give up the hope of one day being able to COMPLETELY account for brain functioning (e.g., intelligence) in terms of neurochemistry and electrophysiology of neurons, just as (most) physicists long ago learned to live with the wave/particle duality of light, giving up the hope of accounting for waves in terms of particles, or vice versa. [1] Bohr, N. (1958). Quantum Physics and Philosophy -- Causality and Complementarity. In: Philosophy in the Mid-Century, R. Klibansky, ed., La Nouva Editrice, Florence. [2] Rychlak, J. F. (1993). A Suggested Principle of Complementarity for Psychology: In Theory, Not Method. American Psychologist 48(9): 933-942.

S. Ji. 160

Appendix II The Definition of Conformations

Mailing List [email protected] Message #3879 From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: Conformation vs. configuration

Date: Mon, 06 May 2002 13:00:46 -0400

To: complex-science

(It seems to be a common practice among molecular biologists to ignore) the fundamental distinction between CONFORMATON and CONFIGURATION well known in physical organic chemistry [see my criticism in BioSystems 44: 17-39 (1997)]. Physical organic chemists are meticulous about distinguishing between CONFORMATION and CONFIGURATION. Confusing these two terms in chemistry is comparable to confusing protons and neutrons in physics and first (words --> sentences) and second (letters --> words) articulations in linguistics, the only difference being that the latter confusions rarely occur in physics and linguistics while the former does, too often in my opinion, in molecular biology and biophysics. For example, it is very common to hear experts in X-ray crystallography of biopolymers or in signal transductions say that the "phosphorylation of group X in protein Y produced CONFORMATION changes." Such a statement is strictly speaking is incorrect. The correct way of expressing it entails replacing CONFORMATION with CONFIGURATION. To explain why, all I have to do is to indicate how these two terms are defined in physical organic chemistry: = The arrangement of atoms in a molecule that cannot be changed without breaking or forming at least one COVALENT bond (i.e., an electron pair shared by two nuclei).

In the following isomerization reaction, the  bond portion of the carbon-carbon double bond must be broken before the trans isomer can be converted into cis isomer, or vice versa: F F F | | | H - C = C - H <------> H - C = C - H | F trans-1,2-difluoroethylene cis-1,2-difluoroethylene S. Ji. 161

(trans isomer) (cis isomer)

= The arrangement of atoms in a molecule that can be changed without breaking or forming covalent bonds. F H F F | | | | H - C - C - H <------> H - C - C - H | | | | H F H H 1,2-difluoroethane 1,2-difluoroethane (trans conformer) (cis conformer) Notice that all that is needed to convert the trans conformer (i.e., conformational isomer) to a the cis conformer is to rotate the carbon atoms around the carbon-carbon single bond relative to each other and no covalent bond is broken nor formed in the process. Configurational changes, involving breaking and forming covalent bonds as they do, are usually slow, activation energy barriers being in the order of several dozen Kcal/mole. In contrast, conformational changes are fast because they implicate the activation energy barriers in the order of thermal energies, i.e, just around 1 Kcal/mole. The biological importance of distinguishing between conformational (also called noncovalent) structures and configurational (or covalent) structures rests on the following facts (in my opinion): 1) All protein-protein and protein-nucleic acid interactions are completely determined by the 3-dimensional shapes of proteins and nucleic acids. 2) Molecular shapes carry molecular information (e.g., the molecular shape of a transcription factor is recognized by and influence the structure and activity of a regulatory segment of DNA). 3) There are two kinds of molecular shapes, denoted as Type I and Type II [see Table 4 in BioSystems, cited above]: 'Type I shapes' can be changed from one shape to another through conformational changes only. 'Type II shapes' can be changed from one shape to another through configuration changes only. 4) Type I shapes are sensitive to microenvironmental conditions (e.g., temperature, pH, ionic strength, electric field gradient, mechanical stress gradient, etc.), while Type II shapes are relatively insensitive. 5) Type I shapes are utilized to transmit information through SPACE, while Type II shapes are used to transmit information through TIME [Theoret. S. Ji. 162

Biol. 130: 239-245 (1988)]. Therefore, I have been led to conclude that one possible reason for there being two kinds of molecular interactions and shapes in molecular and cell biology is so as to mediate information transfer in living systems in SPACE and TIME.

S. Ji. 163

Appendix III. The ‘Apoptosis-Chemiosmosis’ Paradox

Mailing List [email protected] Message #3303 From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: Conformons can resolve the apotosis-chemiosmosis paradox

Date: Sun, 11 Nov 2001 22:17:05 -0500

To: complex-science

Recent experimental work in the field of apoptosis (also known as 'programmed cell death') seems to have established a paradox: 1) In isolated mitochondria and submitochondrial particles, ATP synthesis from ADP and Pi requires proton gradient across the inner mitochondrial membrane (or membrane potential, protonmotive force), as predicted by the chemiosmotic hypothesis proposed by P. Mitchell in 1961 [Nature 191: 144-148 (1961)]. 2) When mitochondria are present in intact living cells undergoing apoptosis, they can synthesize ATP from ADP and Pi without any proton gradient [J. Exp. Med. 185 (8): 1481 - 1486 (1997); Cancer Research 57: 1835-1840 (1997); BBRC 236: 1-9 (1997); Chem.Res. Toxicol. 12:874-882 (1999)]. For convenience of discussion, these apparently contradictory findings will be referred to as the "apoptosis-chemiosmosis paradox". The purpose of this post is to suggest that this paradox can be resolved by the so-called conformon hypothesis of bioenergetics first proposed in 1972 [reviewed in BioSystems 54: 107-130 (2000)]. In other words, recent experimental findings in the field of apoptosis may provide experimental evidence to invalidate the chemiosmotic hypothesis for which Mitchell received the Nobel Prize in Chemistry in 1978 -- in favor of its rival theory based on the concept of conformons [BioSystem cited above]. As is well known, Mitchell proposed that oxidative phosphorylation in mitochondria occurs in two basic steps as shown in Reactions (1) and (2): Respiration ---> Proton Gradient ...... (1) where Respiration indicates the oxidation-reduction reaction wherein redox substrates such as NADH and succinate are oxidized and the electrons and protons resulting therefrom reduce oxygen molecules to form water on the one hand and generate proton gradient across the inner mitochondrial membrane, on the other. Proton Gradient ---> ATP Synthesis ...... (2) S. Ji. 164 where proton gradient drives ATP synthesis from ADP and Pi. Reaction (1) was known to occur before the Mitchell hypothesis was formulated, and so the only substantial novelty of the chemiosmotic hypothesis, as far as I can tell, was the notion that the proton gradient could be utilized as the driving force for the synthesis of ATP, as indicated in Reaction (2). But again, this is nothing new, since (i) the reverse of (2) was known to take place in mitochondria before Mitchell formulated his hypothesis and (ii) the principle of microscopic reversibility in chemical kinetics [K. J. Laidler, "Chemical Kinetics," McGraw-Hill Book Company, New York, 1965, pp. 110-112] guarantees that Reaction (2) should occur. So, when experimental biologists actually demonstrated in the 1960's and 70's that they could drive the synthesis of ATP from ADP and Pi with transmembrane proton gradients, what they had really accomplished is nothing but the experimental confirmation of the validity of the principle of microscopic reversibility in the context of biochemical reactions which chemists had established decades earlier in a purely chemical context. Another component of the chemiosmotic hypothesis concerns the molecular mechanisms proposed by Mitchell to effectuate Reactions (1) and (2), which are highly unrealistic from the points of view of enzymology and protein dynamics. Dissatisfied with the molecular mechanisms proposed by Mitchell, D. E. Green and I formulated what is known as the conformon hypothesis in 1972 [reviewed in BioSystems cited above], according to which oxidative phosphorylation proceeds as follows: ---> Proton Gradient (3) / / Respiration ---> Conformons \ \ ---> ATP Synthesis (4) where conformons are sequence-specific conformational strains of proteins embedded in the mitochondrial inner membrane that harbor both Gibbs energy and genetic information. Mechanisms by which such conformons could be generated from respiration was proposed in 1974 [see Figure 3 in Ann. N. Y. Acad. Sci. 227:211-226 (1974)]. As evident above, according to the chemiosmotic hypothesis, ATP synthesis cannot proceed without the proton gradient, whereas, according to the conformon hypothesis, ATP synthesis can proceed independently of the proton gradient. Now let us turn our attention to what people have found out concerning the relation between the proton gradient (which is inseparably associated with the membrane potential cross the inner mitochondrial membrane) and the ability of mitochondria to synthesize ATP, as studied in the intact cell (and not in isolated mitochondria as was done in the 1960's, 70's and 80's). The critical findings are as follows: (1) Apoptosis requires ATP. That is, apoptosis is an ATP-driven active process [see references given in 2) above]. Apoptosis can be readily measured in cell suspensions or S. Ji. 165 in tissues using fluorescence microscopy and fluorescent dyes that are specific for normal and apoptotic cells. (2) Even under the experimental conditions where mitochondria are known to be the only source of ATP, cells are observed to undergo apoptosis [see above references]. 3) Most, if not all, apoptoses occur if and only if the mitochondrial inner membrane is depolarized or made permeable so that all ion gradients across the inner membrane are dissipated, including the proton gradient [Trends in Cell Biology 8: 267-271(1998)]. These observations clearly demonstrate that mitochondria are able to synthesize ATP under the conditions where the inner mitochondrial membrane is made permeable and hence cannot support any proton gradient. This observation obviously contradicts the basic tenet of the chemiosmotic hypothesis. In contrast, these observations are not incompatible with the conformon hypothesis of oxidative phosphorylation. According to this hypothesis, the primary purpose of the proton gradient generated by respiration is not to synthesize ATP (which it can under appropriate conditions) but rather for mitochondria to communicate with the cytoplasm [Molecular Theories of Cell Life and Death, S. Ji, ed., Rutgers University, New Brunswick, 1991, pp. 60-61] so as to control ATP synthesis in response to the ATP needs of the cell. Thus mitochondria have dual roles to play -- as the provider of ATP and as the sensor of the cytosolic demand for ATP. Such a dual role of mitochondria would be observable only within intact cells, not with isolated mitochondria, which were the main experimental system employed to obtain experimental data to support the chemiosmotic hypothesis in the past. Despite the theoretical and experimental weaknesses that were criticized by some workers in the field [e.g., R. J. P. Williams, Ann. N. Y. Acad. Sci. 227: 98-107 (1974); S. Ji, in "Structure and Function of Biomembranes, K. Yagi, ed., Japan Scientific Societies Press, Tokyo, 1979, p. 34], this hypothesis has been universally adopted by almost all biochemistry textbooks now in print. This state of affairs probably was aided considerably by the awarding of the 1978 Nobel Prize in Chemistry to Mitchell, which 'legitimized' the chemiosmotic hypothesis to such an extent that it now has become an unquestionable theory in the minds of many biologists. But this situation will probably not last much longer, because of the new data that are rapidly accumulating in the field of apoptosis, shedding new light on the structure and function of mitochondria in their native environment, namely, inside the living cell. S. Ji. 166

Appendicx IV. Decoding the DNA Text (Posted for the NECSI list on 2/22/01) With the arrival of the last week's issue of Science, the Celera Genome Map is now practically in every biologist's fingertip. I put mine up over one of my book cases to admire and study with awe and fascination. A similar situation might have transpired in the early 19th century when Egyptologists all over Europe received a copy of the picture or some replica of the Rosetta Stone discovered by Napoleon's soldiers in Egypt in 1799. It may well turn out that the breaking of the code of the human genome (i.e., the DNA text) will follow more or less a similar path followed by the decoding of the Rosetta Stone by Jean-Francois Champollion (1870-1832) in 1822, about 2000 years after the Stone was inscribed on in three different scripts in 196 BC -- in Egyptian hieroglyphs which were unknown and in demotic glyphs and Greek which were known. The key to decoding the unknown glyphs in terms of the known ones was provided by the accidental acquisition by Champollion of a piece of evidence (i.e., the cartouche given to him by one of his friends, carrying the name of Pharaoh Ramsey written in Coptic) which strongly suggested to Champollion that Egyptian hieroglyphs might be "phonograms" and not "ideograms" as had long been thought by Egyptologists since the times of Greeks and Romans. It must have been like a Copernican revolution, turning the whole research orientation in Egyptology upside down. In this post, I would like to make the following two major points for possible comments and criticisms: (1) A Copernican revolution similar to the one experienced by Egyptologists in the 18th century may be needed in order for the biologists of the 21st century to decode the human genome. (2) The DNA text may be "ideograms" for the human brain and "phonograms" for the living cell. The second point seems to be supported by the following theoretical and empirical evidences: (i) According to biocybernetics, a general molecular theory of biology [see "Chapter One" under "5. Others" at http://www.rci.rutgers.edu/~sji], all biopolymers, including DNA, carry not only INFORMATION but also mechanical ENERGY (e.g., DNA supercoils), in the form of sequence-specific conformational strains called conformons [BioSystems 54:107-130 (2000)]. The concept of conformons proposed in 1972 was directly supported almost two decades later by the work of C. Benham who showed that conformons, or equivalently what he called "SIDD's" (stress-induced duplex destabilizations), can indeed accumulate in sequence-specific loci within undertwisted circular DNA duplexes [PNAS 90:2999-3003 (1993)]. S. Ji. 167

(ii) The cell language theory [BioSystems 44:17-39 (1997)] suggests (a) that cells use a molecule-dependent 'microscopic' language very similar (or isomorphic) in principle to sound- and visual signal-based 'macroscopic' human language, and (b) that DNA carries three kinds of genes-- the lexical, the syntactic, and the semantic genes, which are identified, respectively, with structural and associated regulatory genes, the DNA as a whole, and the noncoding regions of DNA [Ann. N.Y. Acad. Sci. 870:41104217 (1999)]. The concept of 'semantic genes' postulated to reside in noncoding regions is in part supported by the work of N. Amano et al. [Biol. Chem. 378:1397-1404 (1997)] showing that noncoding regions may play functional roles, since the number of noncoding bases per genome increases with the number of transcription factors per structural genes in multicellular organisms but not in unicellular ones. Semantic genes are thought to organize the expression of transcribable genes (words) in time and space to generate what I call "DNA sentences," a sequential exposure of a set of related structural genes in a time-ordered manner to transcription factors and polymerases to effectuate their expression, all driven and corralled by conformons, or SIDD's of Benham. Based on these evidences, it may be reasonably concluded that the (genetic) information carried by DNA is comparable to "ideograms" which hieroglyphs were once thought to be and the (conformational) energy stored in DNA is comparable to "phonograms" (which Champollion eventually discovered some of the hieroglyphs to be). All these diverse analogies and connections are summarized in Table 1. Table 1. A comparison between the human genome and of the Rosetta Stone.

Human Genome Rosetta Stone

Produced 2~3 million years ago 2,000 years ago

Discovered 20th century 1799 in

Unknown DNA text written in Hieroglyphic text written text deoxyribonucleotides in pictographs on the surface in the nucleus of the cell of the Rosetta Stone

Known text The Celera (or other) Demotic and Greek glyphs Human Genome Map

Connection Biocybernetics and the cell The cartouche containing the name language theory suggesting that the of Pharaoh Ramsey written in DNA texts are both “phonograms” Coptic indicating that Egyptian S. Ji. 168

(carrying energy) and “ideograms” hieroglyphs are not “ideograms” (carrying information) but “phonograms”

Copernican The DNA texts are “ideograms” Hieroglyphs are not “ideograms” revolution externally (i.e., for the human brain) but “phonograms”. and “phonograms” internally (i.e., for the cell).

Finally, I would like to point out that the above table is consistent with Peircean semiotics (the scientific study of signs), according to which (i) a sign is anything that stands for something other than itself and (ii) a sign consists in an irreducible triad of (a) the sign vehicle (also called sign or representamen), (b) the object referred to by the sign vehicle, and (c) the interpretant (i.e., whatever is induced by a sign in its receiver, which is essential for making a connection between the sign and its object). We can represent this so-called Peircean sign triad geometrically as follows: Sign Vehicle

...... (1)

Object Interpretant The triangle represents the irreducibility of the three elements of the sign. Using the same format, I would suggest the following semiotic relation among some of the key elements of Table 1: Celera Human Genome Map (Macrosemiosis)

...... (2)

DNA Text Isomorphism between (Microsemiosis) Cell and Human Languages (Due to Micro-Macro Coupling in the Human Brain) Triangle (2) indicates that the three elements of DNA located at the three vertices are irreducible similarly to Triangle (1). Triangle (2) also suggests that the human brain can be viewed as a Rosetta Stone, in which are represented two kinds of glyphs -- the known (e.g., the Celera Human Genome Map) and the unknown (i.e., the DNA text in the nucleus of the cell) which contemporary molecular biologists are trying to decode. S. Ji. 169

The compatibility of Table 1 with semiotic principles is another support for the validity of comparing decoding of the DNA text with the decoding of Egyptian glyphs. One philosophical or metaphysical spin-off of Triangle (2) may be that the human brain is a Rosetta Stone carrying both microglyphs (DNA and neuronal processes) and macroglyphs (linguistic capacity, the Universal Grammar of Chomsky, etc.). S. Ji. 170

Appendix V. An Intellectual Crisis in the Field of DNA Microarray Data Analysis

Mailing List [email protected] Message #5511From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: An intellectual crisis in the field of DNA microarray data analysis

Date: Tue, 04 Nov 2003 16:29:14 -0500

To: complex-science

During the past two days, I have been attending a workshop in bioinformatics held at Rutgers. Researchers in the field of DNA microarray data analysis, ranging from young graduate students to established bioinformaticians, used the terms "mRNA level" and "gene expression" synonymously and interchangeably. When I pointed out to some of them after their lectures why I think these terms are not the same and how conflating them could lead to misinterpretations of DNA microarray data, they all seemed to agree with me.

It is not too difficult to understand why mRNA levels and gene expression rates are not the same: You can have a mRNA level rise inside the cell without any increase in the expression of the corresponding gene, if the rate of mRNA hydrolysis is decreased, which can happen independently of any gene expression. If anyone has any doubts about my conclusion here, just think about the "checking account balance-income metaphor" that I used in my 10/29/03 post: It is impossible to determine someone's income (rates of gene expression) from his checking account balance (mRNA levels).

If the explanation for the difference between mRNA levels and rates of corresponding gene expression is so simple, how is it that so many, if not all, biologists and computational scientists working in the DNA microarray area are uniformly conflating these two terms?

I have the following possible answers: 1) Microarrays are constructed using cDNA produced from genes. Since mRNA molecules are identified through the use of such cDNA, mRNA molecules are indirectly related to genes within the context of microarray experiment. Of course, everybody knows that mRNA is synthesized using DNA as template inside the cell, which also contributes to the myth of the mRNA-gene connection. 2) It sounds more important and impressive to say that microarrays measure genes rather than mRNA levels S. Ji. 171

3) Computational scientists like to simplify biological problems for mathematicaltreatment. 4) Molecular biologists tend to be awed by anything mathematical and tend to accept whatever their mathematician or computational colleagues say about the results of their analysis of DNA microarray data (conflating mRNA levels with gene expression, of course). 5) Because of the immense complexity involved, both with respect to the biological systems being studied and with respect to the amount and quality of data being analyzed, workers in this area may have unconsciously given up the hope of ever solving their problems through the use of critical questioning, heated debates, and rigorous logical analysis of empirical data. The confusions and despairs that I now sense in the field of DNA microarray may be a blessing in disguise. After enough frustrations and failures to produce practical biomedical applications eagerly awaited by millions of investors around the world, both biomedical scientists and granting agencies may come to realize one day what is really missing -- the COMPUTER MODEL of the LIVING CELL (CMLC). I truly believe that, without CMLC, it would be impossible to extract any biologically meaningful information out of DNA microarray data. A similar situations happened in physics. Physicists in the 19th and 20th centuries could not meaningfully interpret atomic spectral data without the Bohr model of the atom formulated in 1913. If biologists can succeed in constructing a working CMLC within the next 10 years, biology would be behind physics by just one century, which may be viewed as a reasonable lag time. S. Ji. 172

Appendix VI. Laws of Miroarray Data Interpretation (I).

Mailing List [email protected] Message #7074 From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: "Laws of microarray data interpretation"

Date: Mon, 20 Sep 2004 19:19:03 -0400

To: complex-science

About a year ago (see the attached post NECSI Message #4902 dated 13 May 2003), I formulated 5 general statements that I claimed to apply to interpreting microarray data. The post also gives a brief description of the revolutionary microarray technique which promises to usher in a new era in molecular and cell biology, medicine, and pharmaceutical industry in the coming decades.

The purpose of this post is to bring to your attention a paper just published by J. Garcia- Martinez and his colleagues at the University of Valencia, Spain ["Genomic Run-On Evaluates Transcription Rates of All Yeast Genes and Identifies Gene Regulatory Mechanisms." Mol. Cell 15: 303-313 (2004), which can be located by Googling "Garcia- Martinez, genomic run-on"]. They simultaneously measured both TR and mRNA levels (also called transcript levels) of about 6,000 genes in glucose-starved yeast, using cDNA arrays and radiolabeling techniques. Their figures 3 and 4 in the cited paper clearly demonstrate that the time profiles of TR and mRNA levels do not coincide, leading to the conclusion that TR (fluxes) and mRNA levels (steady-state levels) change more or less independently of each other as predicted in my posts dated May 13, 2003 (cited above) and July 15, 2002 post (see below).

Thus, the experimental results of Garcia-Martinez et al support not only the general statements formulated in May, 2003 but also my contention made in 2002 that most of the workers in the field of microarrays unwittingly conflated steady-state levels and rates ("The 'steady-state level' vs. 'flux' confusion", NECSI Message #4082 dated 15 July 2002).

If the theory of interpretation of microarray data that I proposed in the past couple of years is basically correct, it may have two major consequences: (1) Most, if not all, of the microarray experiments published since 1995 may have to be re-interpreted. (2) The new perspective/paradigm may lead to developing more sophisticated and realistic mathematical, kinetic, and statistical methods for mining valuable biological information embodied in microarray data, that will allow us to identify, S. Ji. 173 for example, transcription units coding for siRNAs (or microRNAs) and riboswitches [1,2,3]. References: [1] Mattick, J. S. (2004). The Hidden Genetic Program of Complex Organisms. Sci. Am., October, 2004, pp. 60-67. [2] Stix, G. (2004). Hitting the Genetic OFF Switch. Sci. Am., October, 2004, pp. 98-101. [3] Gibbs, W. W. (2003). The Unseen Genome: Gems among the Junk. Sci. Am., November, 2003, pp. 48-53

S. Ji. 174

Appendix VII. The Laws of Microarray Data Interpretation (II)

Mailing List http://necsi.org:8100/Lists/complex-science/List.html #4092

From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: The "laws" of microarray experiments

Date: Tue, 13 May 2003 22:41:33 -0400

To: complex-science

Microarray (or more generally cDNA array, including macroarrays) techniques invented by biologists at Stanford in 1995 are one of the most exciting developments in molecular biology of the 20th century. But, because of the "theoretical" difficulties inherent in interpreting what these elegant techniques measure, they have not yet had any major impact in biology or medicine, in my opinion.

To help make these techniques more useful to biologists and biotechnologists, it seems necessary to rigorously separate true from erroneous statements in interpreting what microarray techniques measure. The purpose of this post is to suggest a list of what appears to me to be generally valid statements concerning the capabilities of microarray techniques.

As most members of this list would know by now, microarray techniques measure thousands of mRNA levels in the cell simultaneously. A microarray typically consists of a microscopic slide with 10,000 squares within an area of about 1~2 cm2. Each square or element is about 100 microns by 100 microns. Using a robotic pipette, it is possible to deposit a liquid droplet on each of these squares that contains a DNA fragment that can recognize its complementary DNA fragment. Since the latter can be synthesized from the mRNA molecules isolated from living cells using reverse transcriptase, a microarray can measure typically 10,000 mRNA molecules in the living cell.

So far so good.

But the trouble begins as soon as people substitute the word "mRNA" with the phrase "gene expression" without any qualifications. Many seem to think that this substitution is justified because gene expression leads to mRNA synthesis, thus leading to the now widely and uncritically accepted "slogan" that “microarrays measure gene expression."

It does not take much reflection to realize that this last statement cannot be valid generally. This is because the mRNA molecules are unstable, the half-lives ranging from minutes to hours, so that the mRNA levels inside the cell are determined not only by S. Ji. 175 mRNA synthesis (i.e., gene expression) but also by mRNA hydrolysis (or destruction). A similar situation occurs with our checking account: The balance (i.e., mRNA level) in our checking account at any time is determined not only by our income (gene expression) but also by our expenditure (mRNA hydrolysis). It is obvious that the balance in our checking account can increase even in the face of a reduction in our income, if we reduce our expenditure even more. In other words, there is no direct proportionality between our income (gene expression) and the amount of the balance (mRNA level) in our checking account, except when our expenditure is zero.

In conclusion, we can make the following general statements that seem valid concerning microarray experiments. For convenience, we may refer to them as the "laws" or "rules" of microarray experiments: I) Microarray techniques (as usually employed) measure mRNA levels. II) mRNA levels in the cell are determined by both the rates of gene expression and mRNA hydrolysis. III) Microarray techniques can measure (rates of) gene expression if and only if the rate of mRNA hydrolysis is negligible compared to that of mRNA synthesis. IV) If experimental perturbations affect the rates of both mRNA synthesis and hydrolysis more or less equally, then the same perturbations can increase or decrease gene expression rates without affecting the RNA levels. Under such conditions, microarray techniques will fail to measure gene expression. V) Unless mRNA levels and rates of mRNA hydrolysis are measured simultaneously, no inference can be drawn about the rates of gene expression solely from microarray experiments. A quick perusal of the recent articles on microarray experiments published in Bioinformatics and Nature Reviews Genetics indicate that mot, if not all, of them seem to violate one or more of the above putative "laws" of microarray experiments.

S. Ji. 176

Appendix VIII. The Algebra of Complementarism

Mailing List [email protected] Message #4510 From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: The algebra of complementarism

Date: Sun, 05 Jan 2003 23:11:43 -0500

To: complex-science

Complementarism is a new philosophical framework inspired by both quantum physics and molecular biology of the 20th century [1]. Just as physicists found in the 1920's that light can be viewed as a complementary union of waves and particles, so I found in the early 1980's that life can be viewed as a complementary union of (free) energy and (genetic) information [2,3]. The complementary union of information (gn-) and energy (- ergy) was given the name, 'gnergy'. The concept of gnergy was then extended beyond biology to cosmology, leading to the postulate that all self-organizing processes in the Universe, including the Big Bang and the origin of life, is driven (i.e., initiated and maintained) by gnergy.

Complementarism has two aspects -- the ontological and epistemological: 1) "The ultimate reality perceived and and communicated by the human brain is a complementary union of irreconcilable opposites." [1, p. 525]. 2) "The 'complementarian' or 'complementarist' epistemology contains the following elements: (1) The human brain perceives the ultimate reality through a cooperative action between the right and left hemispheres. (2) To communicate the perceived reality to others, humans use predominantly the left or right hemisphere at a given time and cannot use both simultaneously. (3) The left and right hemispheres have relatively distinct psychological and psychical functions . . . " [1, p. 525] that are complementary to each other. As I stated in [1, 525], "One of the most characteristic features of complementarism is its critical reliance on the biology of the human brain, namely, the complementary nature of the hemispheric specializations o the human brain (Springer and Deutsch, 1989; TenHouten, 1978; Jaynes, 1976; Edwards, 1989). Therefore, complementarism is radically biology-based, perhaps the first philosophical system ever constructed on the S. Ji. 177 foundation of the modern biological knowledge of the human brain. The biological basis of complementarism is manifested both at the molecular biology level (i.e., information/energy complementarity in enzymes) and at the medical science level (i.e., the hemispheric specialization of the human brain)." [1, 5425-526]. Another interesting feature of complementarism is that its epistemological content can be unambiguously represented using algebraic expressions as shown in Appendix F in [1, pp. 545-547]. This is reproduced below in the hope that I may receive some critical comments or suggestions from the members of this list. In addition, I took the advantage of this opportunity to correct some typos (i.e., X's mis-typed as x's) in the original article so as to avoid possible confusions in the mind of future readers. References: [1] Ji, S. (1995). Complementarism: A Biology-Based Philosophical Framework to Integrate Western Science and Eastern Tao. In: Psychotherapy East and West: Integration of Psychotherapies. Korean Academy of Psychotherapists, 178-23 Songbuk-dong, Songbuk-ku, Seoul. Pp. 518-548. [2] Ji, S. (1985). The Bhopalator: A Molecular Model of the Living Cell. Asian J. Exp. Sci. 1: 1-33. [3] Ji, S. (1991). Biocybernetics: A Machine Theory of Biology. In: Molecular Theories of Cell Life and Death ( Ji, S., ed.), Rutgers University Press, New Brunswick, pp. 1-237. ------The Algebra of Complementarism (Appendix F in [1]) If we designate the form of reality communicated to 'other' by the left hemisphere as L (e.g., narratives, mathematical expressions, etc.), and that communicated by the right hemisphere as R (e.g., musical melodies, paintings, dancing, etc.), then we may express the above epistemological argument more clearly using 'algebraic' symbols as follows; (lh x rh) U ------> P (1) (lh) P ------> L (2) (rh) P ------> R (3) where 'lh' and 'rh' stand for the left and right hemispheres, respectively, 'U' stands for the ultimate reality that gives rise to the perceived reality 'P', 'x' symbolizes the 'complementary interactions' between the left and right hemispheres that underlie our perception of the ultimate reality, '------>' denotes a causality, and (A)B indicates that A operates on B. Process (1) represents an input to the brain, namely the transduction of U outside the brain into P inside the brain (i.e., patterns of neuronal firings and connections ?) mediated by the whole brain, lh x rh, and hence a kind of measurement, while Processes (2) and (3) signify outputs of the brain and hence 'communication' to other, mediated by either lh or S. Ji. 178 rh. It would be convenient to define an operator 'X' in such a way that (2) X (3) signifies: ( (lh) P) X ( (rh) P) ------> L X R (5) and U = L X R (6) Process (6) indicates that ultimate reality outside individual brains, U, is a complementary union of communicated realities L and R. To make Equation (6) follow logically from (5), it may be necessary and sufficient to make the following two assumptions: ( (lh) P X ( (rh) P) = ( lh X rh ) P (7) and (lh X rh ) P ------> U (8) The operator 'x' (small x) may roughly correspond to cognitive mechanisms underlying perception that operate primarily within individual brains, while the operator 'X' (large X) depends on communication and rational thinking that operate on the social and cultural levels involving a society of individuals. Based on this conjecture, the following corollary may be formulated: Evolution lh x rh ------> lh X rh (9) Communication Process (9) is best interpreted as implying that the individual brains of Homo sapiens have evolved, through communication, the capability of operating in two distinct functional modes -- the individual mode (x) and the group mode (X). It is interesting to note that, when human brains operate in the individual mode (x), Process (1) cannot be reversed; it is only when human brains operate in the group mode (X), can Process (1) be reversed, as indicated by Process (8), and hence the understanding is possible that U is a complementary union of L and R, Equation (6). The fact that complementarism can be expressed algebraically at all -- incomplete as it is -- may be significant, because this may reflect a high degree of logical consistency inherent in the new philosophical system.

S. Ji. 179

Appendix IX. Peircean Signs as Gnergons

Mailing List [email protected] Message #4454 From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: Peircean signs as 'gnergons'

Date: Thu, 19 Dec 2002 18:51:59 -0500

To: complex-science

(Abstract submitted to The Third International Conference on Semiotics, Victoria College, University of Toronto, October 17-19, 1997) GNERGONS AS QUANTA OF SEMIOSIC ACTIONS. Sungchul Ji, Department of Pharmacol. and Toxicol., Rutgers University, Piscataway, N.J. 08855.

Gnergons are defined as discrete physical entities, ranging in size from the microscopic to the macroscopic, that carry both energy (to do work) and information (to control work) in complementary union. It is thought that gnergons are essential for all organized processes in nature, including semiosis, the process of information transduction and transmission mediated by signs. There are two kinds of gnergons now known in biology -- conformons (sequence-specific conformational strains in proteins, RNA and DNA) and IDS (intracellular dissipative structures; e.g., the intracellular calcium ion gradient), which together provide the requisite free energy and information for supporting cell functions. An analysis of the data recently published by C. Benham (J. Mol. Biol. 255, 425-434 (1996)) indicates that conformons in DNA carry 450-2,500 Kcal/mol of free energy and 200-600 bits of Shannon information (S. Ji, manuscript submitted; later published in Ji, S. (2000). Free energy and information contents of Conformons in proteins and DNA. BioSystems 54:107-130; my addition). Based on the finding that 9 distinct classes of conformons are necessary and sufficient to account for all molecular biological processes, from enzymic catalysis to gene expression and from the origin of life to cell functions (S. Ji, Molecular Theories of Cell Life and Death, Rutgers University Press, 1991, pp. 40-43), it was concluded that conformons are quanta of biological actions -- i.e., the minimal units of information and energy required for living processes on the molecular level. Similarly, it is suggested here that there exists most likely an indefinitely large set of gnergons that underlies semiosic action on the societal and cultural levels.

S. Ji. 180

Appendix X. ‘Cosmolanguage’ (I)

From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: The 'Nashuator': A Model of Biological Evolution

Date: Thu, 15 Jun 2000 19:06:14 -0400

To: complex-science

Stan Salthe wrote (>) on 6/12/00:

(3) On structural attractors being in place before the origin >of life -- yes, just as physics and chemistry were in place, and >just as universal constants were. These attractors entrain >relatively simple forms in abiotic systems (trees vortices), but >would lead to much more complicated forms in the context of >genetic constraints. In this sense, yes, evolution is, to a >large extent, predetermined, but history does play a role. Every >actual system is unique, but does have generic characteristics.

It seems to me that most of what Stan has been saying about evolution may be supported by a model of evolution that is suggested by the principle that appears to be playing a fundamental role in all living systems. I suggest that this principle is the so-called ‘ rule governed creativity’ , the notion that the human brain can generate an indefinitely large number of novel and meaningful sentences based on finite sets of words and grammatical rules. The ‘ rule-governedness’ is due to the finiteness of the sets of words and grammatical rules, while the ’ creativity’ is associated with the vastness of the number of possible sentences that can be generated from such finite sets, leading to an impossibility of predicting (or knowing) what sentence a speaker would utter the next moment, no matter how well one knows the speaker.I have recently applied the concept of rule-governed creativity and associated linguistic laws and principles to S. Ji. 181 accounting for (i) protein folding, (ii) signal transduction cascades, (iii) the living cell, and (iv) biocomplexity in general (presented at ICCS3).

The purpose of this post is to extend the principle evolution in the following manner: 1) All rule-governed creative systems (called ‘ creatons’ ) are considered to be selected from their precursors, which will be called ‘ rule-governed chaotic systems’ or ‘ chaons’ (?) for short. The maximum information content of rule- governed creative systems, Icreaton, will be determined by the number of rule- governed chaotic systems Wchaons out of which creatons are selected.

Icreatons = log2 Wchaons ...... (1)

2) Rule-governed chaotic systems include ‘ deterministically chaotic systems’ studied in physics, chemistry, and mathematics.

3) It is postulated that rule-governed chaotic systems evolved from pure chaos.

4) Combining these postulates, we obtain the following model of evolution:

Cosmogenesis: Chaos ---> Rule-governed chaotic systems . .(2)

Biological Evolution: Rule-governed chaotic systems ---> Rule-governed creative systems . . . (3)

Cultural Evolution: Rule-governed creative systems ---> Natural language ...... (4)

Processes (2), (3) and (4) are all self-organizing in the Prigoginian sense (i.e., in the sense of irreversible thermodynamics and statistical mechanics). Rule- governed chaotic systems are identified with ‘ strange attractors’ ; rule- governed creative systems with ‘ living organisms’ governed by ‘ cell S. Ji. 182 language’ and biosemiotic principles; and natural language is identified with human language, the origin of the concept of rule-governed creativity.

We can summarize this ‘ linguistic model’ of evolution as:

Chaos ---> RGChS ---> RGCrS ---> Human language . . . . . (5) where RGChS stands for rule-governed chaotic systems, and RGCrS indicates rule-governed creative system. True to its name, Equation (5) can be made to embody another linguistic principle, namely, the principle of double articulation (first articulation = formation of sentences from words; second articulation = formation of words from letters), if we can make the following identifications:

Chaos = Letters RGChS = Words RGCrS = Sentences Human Language = Texts

If these identifications are valid, we may be able to conclude that the principles of language (and associated semiotic principles of Peirce, including rule-governed creativity and double articulation) are manifested at two levels ? at the material level in the external world as wells as at the mental level in the internal world. We may refer to this phenomenon as the ‘ principle of the dual manifestations of language or semiosic principles’ , or the ‘ language duality’ , for short. Like the wave/particle duality in physics, this matter/mind duality may be a reflection of a deep-lying complementarity which may be identified with the following triad:

S. Ji. 183

Cosmological Language

Material Language Mental Language which may be alternatively expressed as follows, using the Peircean semiotics (the study of sign) terminology:

Cosmosemiosis

Cytosemiosis Anthroposemiosis (Physiosemiosis)

The model of evolution outlined here (see Equation (5)) appears to provide a broad theoretical framework of evolution in which a large number of concepts,principles, laws, and theories from physics, chemistry, biology, linguistics, semiotics, and philosophy (e.g., chaos, attractors, cosmogenesis, self- organization, complexity, thermodynamics, rule-governed creativity, semiotics, Merleau-Pontyan ontology of the Flesh, etc.)often discussed in connection of evolution may be organized and integrated into a coherent whole. I propose that the postulated model of biological evolution embedded in Equation (5) be referred to as the ‘ Nashuator’ to emphasize or ‘ prescind’ the self- organizing aspect of the proposed model. This follows the well-established tradition in physical chemistry that all self-organizing systems be named as ‘ the S. Ji. 184

X-ator’ , where X is the name of a city connected in one way or another with the self-organizing systems being named (e.g., the Brusselator, Oregonator, etc.).

S. Ji. 185

Appendix XI. ‘Cosmolanguage’ (II)

Mailing List [email protected] Message #5069 From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: A universal definition of languages

Date: Sun, 03 Aug 2003 23:38:18 -0400

To: complex-science

In formal language theory, a language is defined as a set of strings of symbols generated from an alphabet obeying a set of rules called grammar [1]. Clearly this definition was motivated by the desire to model natural language. In 1997, I formulated the concept of cell language as a means to account for the mechanisms underlying living processes on the molecular and cellular levels [2, 3]. The main objective of this post is to propose a universal definition of language that not only accommodates both these languages but also suggests other kinds of languages that may not have been clearly recognized so far. The proposed new definition for language can be derived from the definition of formal language by applying the following two rewriting rules: 1) Replace "symbols" with "signs". Signs are defined as a 5-tuple, (G, P, R, O, I) in my July 29 post, where G = gnergy, P = sign processor, R = representamen, O = object, and I = interpretant. 2) Replace "strings" with "networks". Networks are defined here as systems of signs (or nodes) connected by edges with varying degrees of coupling, ranging from -1 to +1. Connecting a pair of nodes with an edge is identified with "concatenation" in formal language theory, which was generalized into the N-dimensional semantic/properties /functions space in 1991 [4]. The resulting definition reads: "A language is a network of signs." (1) Table 1 lists some examples of languages defined by Expression (1). The first column indicates the names of languages and their possible dimensionality, N, which is given in the parenthesis. The number N refers to the degree of freedom available to a language. For example, natural language is considered to be a 1-dimensionl (or linear) concatenation of symbolic signs, whereas cell language is postulated to be a 6- dimensional concatenation of atoms and molecules in space and time [4]. Mathematical language can be considered as N-dimensional, where N can be any natural number, from S. Ji. 186

1 to infinity. The second column explicates the source of information and free energy (i.e., Gnergy) responsible for reifying or effectuating the language. Formal and written languages (e.g., in the form of a written text) are passive in that the sign processor, the source of gnergy, are external to texts, whereas spoken language is active in the sense that the gnergy- driven sign processing and the being of signs are synchronous and inseparably linked. The third column lists the sign processor (P), an absolute requirement for any language and yet often ignored or taken for granted in most discussions on languages and semiotics, thereby often leading to unnecessary paradoxes and confusions. The fourth column indicates the type of sign vehicles (R) that Peirce recognized. According to him, there are three and only three types of signs which he called iconic (e.g., pictures, diagrams), indexical (e.g., smokes, weathervanes), and symbolic (e.g., numbers, words). The associated objects (O) and interpretants (I) were omitted in order to save space. Probably the most surprising and also potentially most controversial conclusion is given in the last row in Table 1, where I tentatively (as evidenced by the question mark) suggest the possible existence of a language which may be referred to as the "Universal language" or "cosmological language". We may think of it as the language that the Universe uses (?) to represent Itself to Itself (of which the Homo sapiens happens to be a part). Such a Universe was referred to as the Shillongator, or the Self-Knowing Universe in 1991 [4, p. 236]. If this perspective is right, the Universe has its own language, 'Cosmolanguage' for short, out of which all other languages may have evolved, including cell and human languages, logics, mathematics, formal languages, art, and music! Table 1. Examples of languages defined as networks of signs. ______Language Gnergy Processor Representamen (N = ) (G) (P) (R) ______1) Natural Active Humans Symbolic/Indexical Signs (1) (Spoken) Passive Humans Symbolic Signs (Written) 2) Formal Passive Humans Symbolic Signs (1) 3) Mathematical Passive Humans Iconic Signs (N) 4) Cell Active Cells Indexical Signs (6) S. Ji. 187

5) Visual Passive Animals Iconic Signs (3 ?) 6) Musical Active Humans Iconic/Indexical Signs (4?) 7) Universal Active Universe Iconic/Indexical/ (10?) Symbolic Sign (?) ______The various languages listed in Table 1 can probably be arranged into a hierarchy showing either their order of evolution (ontological hierarchy) or of complexity (epistemological hierarchy?). One possible ontological hierarchy would be: 7) --> 4) --> 6) --> 5) --> 1) --> 3) --> 2) ...... (2) Within each ontological linguistic hierarchy, there may exists a unique epistemological hierarchy. The epistemological hierarchies are therefore thought to be more difficult to formulate in general terms. The Chomsky hierarchy (i.e., rewriting systems, finite-state grammar, context-free grammar, context-sensitive grammar) may be considered as an epistemological hierarchy belonging to the ontological hierarchy of formal language. We may for convenience refer to the Chomsky hierarchy as a 'horizontal linguistic hierarchy' and the ontological hierarchy suggested in Expression (2) as the 'vertical linguistic hierarchy'. References: [1] Book, R. V. (1973). Topics in Formal Language Theory. In: Currents in the Theory of Computing (Aho, A. V., ed.), Prentice-Hall, Inc., Englewood Cliffs, N.J., pp. 1-34. [2] Ji, S. (1997). Isomorphism between cell and human languages: molecular biological, bioinformatic and linguistic implications. BioSystems 44:17-39. [3] Ji, S. (1999). The Linguistics of DNA: Words, Sentences, Grammar, Phonetics, and Semantics. Ann. N.Y. Acad. Sci. 870:411-417. [4] Ji, S. (1991). Biocybernetics: A Machine Theory of Biology. In: Molecular Theories of Cell Life and Death (Ji, S., ed.), Rutgers University Press, New Brunswick, pp. 1-237. See Table 1.6.

S. Ji. 188

Appendix XII. Taxonomy of Entropy-Information Relations

Mailing List [email protected] Message #6768 From: Sungchul Ji

Sender: (Yaneer Bar-Yam)

Subject: Taxonomy of entropy-information relations based on the complementarian logic

Date: Fri, 04 Jun 2004 09:51:05 -0400

To: complex-science

In my previous post, I discussed what entropy-information relations ARE NOT (e.g., not NPI). In this post, I will discuss what I think entropy-information relations ARE, as deduced from applying the complementarian logic to the problem. 1) The terms 'entropy' and 'information' occur widely in physics [1], mathematics [2], philosophy [3], computer science [4, 5], biology [6] and cosmology [7, 8, 9]. From these literatures, we can recognize the following three main varieties of entropies and informations. These are listed below more or less in the chronological order of their discovery or emergence in the history of the Universe: i) THERMODYNAMIC ENTROPY = Reversibly absorbed heat divided by the temperature at which the heat absorption occurs. Discovered by Clausius in 1865 ii) STATISTICAL MECHANICAL ENTROPY = Natural l ogarithmic function of the number of microstates compatible with a given macrostate postulated by Boltzmann in 1877 to underlie thermodynamic entropy iii) MATHEMATICAL ENTROPIES = Mathematical entities conforming to the general form, x log x or its variants, where x is a probability distribution, including Shannon entropy formulated in 1948 [4] and Tsalis entropy formulated in 1988 [10]. i) PRIMORDIAL INFORMATION = Information that, together with energy/matter, constitutes gnergy, the primordial driving force for all self- organizing processes in the Universe [12]. In other words, primordial information and energy/matter are the complementary aspects of Gnergy. ii) PHYSICAL INFORMATION = Information about the state of physical systems, similar to or identical with Brillouin's "bound" information [11]. iii) ABSTRACT INFORMATION = Information referring to non-physical entities. Similar to or identical with Brillouin's "free" information [11]. S. Ji. 189

2) It is postulated here that these 6 entities are related to each based on the complementarian logic (discussed on this list on numerous occasions) [13]. To make it easier to understand, these rather complex relations are described in three steps, using Figures 1, 2 and 3. GNERGY

ENERGY/MATTER PRIMORDIAL (Thermodynamic S) INFORMATION Figure 1. Energy/matter and primordial information as complementary aspects of gnergy, the primary driving force of all self-organizing processes in the Universe, including the Big Bang, the origin of life, and the biological evolution. Thermodynamic entropy S is viewed as an integral component of free energy and matter.

PRIMORDIAL INFORMATION

PHYSICAL ABSTRACT INFORMATION INFORMATION (Statistical Mechanical S) (Mathematical S) Figure 2. Physical (bound) information and abstract (free) information as postulated to be the complementary aspects of primordial information. Statistical mechanical entropy may be best regarded as a subset of physical information, and likewise mathematical entropy may be best viewed as a subset of abstract information. GNERGY

ENERGY/MATTER PRIMORDIAL (Thermodynamic S) INFORMATION

PHYSICAL ABSTRACT INFORMATION INFORMATION S. Ji. 190

(Statistical Mechanical S) (Mathematical S)

Figure 3. The taxonomy of entropy-information relations deduced on the basis of the complementarian logic [13]. This figure was obtained by merging Figures 1 and 2 through the common vertex, PRIMORDIAL INFORMATION. 3) The content of Figure 3 can also be represented in a tabular form as shown in Table 1. The numbers in the parentheses in the "entropy vector" (i.e., the uppermost row) and the "information vector" (i.e., the leftmost column) reflect approximately the history of the emergence of different kinds of entropies or informations. Please note that the order of the elements in these vectors are fixed by history, not arbitrary. 4) The notation, R_i,j, indicates the relation between the ith class information and the jth class entropy. There are two kinds of relations in Table 1--allowed and disallowed, denoted as 1 and 0, respectively. 5) Only the diagonal (from upper left to lower right) elements in Table 1 are allowed or realized in nature, others being disallowed. This selection rule can be characterized algebraically as R_i,j = 1, if and only if i = j (1)

Table 1. A summary of the entropy-information relations characterized by Figure 3.

Thermodynamic Entropy (1) Statistical Mechanical Entropy (2) Mathematical Entropy (3)

PRIMORDIAL INFORMATION (1) R_1,1 = 1 R_1,2 = 0 R_1,3 = 0

PHYSICAL INFORMATION (2) R_2,1 = 0 R_2,2 = 1 R_2,3 = 0

ABSTRACT INFORMATION (3) R_3,1 = 0 R_3,2 = 0 R_3,3 = 1

6) Based on Figure 1, we can identify R_1,1 as COMPLEMENTARITY (see [13] for a general definition of this term). Based on Figure 2, we can characterize R_2,2 and R_3,3 S. Ji. 191 as indicating SUBSETHOODS (i.e., statistical mechanical S is a subset of physical information, and mathematical entropy is a subset of abstract information). 7) It should be pointed out that Brillouin's so-called NPI (Negentropy Principle of Information) [11] is equivalent to maintaining that R_2,2 is IDENTITY (rather than SUBSETHOOD, as I claim here).

References: [1] Zurek, W. H., ed. (1991). Complexity, Entropy and the Physics of Information. Addison-Wesley Publishing Co., Redwood, CA. [2] Kolmogorov, A. (1968). Logical Basis for Information Theory and Probability. IEEE Transactions on Information theory IT-14 (5):662-664. [3] Kubat, L. and Zeman, J. (1975). Entropy and Information in Science and Philosophy. Elsevier Scientific Publishing Co., Amsterdam. [4] Shannon, C. E. and Weaver, W. (1949). The Mathematical Theory of Communication. University of Illinois, Urbana. [5] Gray, R. M. (1990). Entropy and Information Theory. Springer-Verlag, New York. [6] Ji, S. (1974). Energy and Negentropy in Enzymic Catalysis. Ann. N. Y. Acad. Sci. 227:419-437. [7] Hawking, S. (1994). Balck Holes and Baby Universes and Other Essays. Bantam Books, New York. [8] Mukhanov, V. F. (1991). The Entropy of Balck Holes. In [1], pp. 47-52. [9] Ji, S. (1991). Biocybernetics: A Machine Theory of Biology. In: Molecular Theories of Cell Life and Death (Ji, S., ed.), Rutgers University Press, New Brunswick. Pp. 1-237. [10] Tsallis, C. (1999). Nonextensive Statistics: Theoretical, Experimental and Computational Evidences and Connections. Brazilian J. Physics 29(1):1-35. [11] Brillouin, L. (1962). Science and Information Theory. Second Edition. Academic Press, Inc., New York. Pp. 152-153. [12] Ji, S. (1991). Biocybernetics: A Machine Theory of Biology. In: Molecular Theories of Cell Life and Death (Ji, S., ed.), Rutgers University Press, New Brunswick, pp.152, 231-234. [13] Ji, S. (1995). Complementarism: A Biology-Based Philosophical Framework to Integrate Western Science and Eastern Tao. The text available at http://www.rci.rutgers.edu/~sji. See p. 524.

S. Ji. 192