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Modern Applications in and

The Synergy between and Cognitive

Matthew X. He

Abstract—Bioinformatics is the comprehensive applications of informatics through a view of emerging pattern, mathematics, science, and a core set of problem-solving methods to dissipative structure, and evolving of living systems. the understanding of living systems. Cognitive informatics studies cognition and that investigates the processes of the natural . This paper briefly reviews the intersections II. BIOLOGICAL DATA and connections between two emerging fields of bioinformatics and cognitive informatics through a systems view of emerging pattern, dissipative structure, and evolving cognition of living systems. A The potential data objects in bioinformatics are illustrated new kind of math-denotational mathematics for cognitive informatics below in Figure 1: is introduced. It is hoped that this brief review encourages further exploration of our understanding of the biological basis of cognition, , , , , and .

Keywords—Bioinformatics; cognitive informatics; patterns; data; denotational mathematics information; discovery

I. INTRODUCTION

Bioinformatics is the comprehensive applications of mathematics, science, and a core set of problem-solving methods to the understanding of living systems. It will have profound impacts on all fields of biological and medical sciences. Cognition is viewed as a process of living systems.

Cognition is an abstract of advanced living Fig. 1 Potential data objects in bioinformatics organisms. It is studied as a direct property of a or of an abstract mind on sub-symbolic and symbolic levels. The general -driven problems in bioinformatics Cognitive informatics studies cognition and information include sciences that investigates the processes of the natural • Find functionally significant motifs in a family of intelligence. As both fields continue their rapid development protein sequences; and progress, it is a central challenge to understand the • Develop techniques to detect alternate genetic codes; biological basis of cognition, perception, learning, memory, • Develop techniques to identify the extent of thought, and mind. horizontal gene and intron transfer; The time seems ripe to bring these varied topics together • Develop techniques to help understand the role of to focus on our understanding of the emerging patterns, DNA repeats in genome evolution. dissipative structures, and evolving cognition of living Over the past few decades, major advances in the field of systems through a process of experimental application, molecular biology, coupled with advances in genomic scientific computation, and theoretical abstraction. This paper technologies, have led to an explosive growth in the biological briefly reviews the intersections and connections between data generated by the scientific community. This deluge of these two emerging fields of bioinformatics and cognitive genomic information has, in turn, led to an absolute requirement for computerized to store, organize, and index the data and for specialized tools to view, analyze, Author is with Nova Southeastern University, Division of Math, Science, and and interpret the data. Bioinformatics is an emerging field of Technology, 3301 College Ave. Ft. Lauderdale, FL 33314 USA science in which biology, , and information (phone 954-262-8310; fax: 954-262-3931; e-mail: hem@ nova.edu). technology merge to form an emerging discipline. The ultimate goal of the field is to enable the discovery of new

ISBN: 978-960-474-363-6 258 Modern Computer Applications in Science and Education biological insights and hidden patterns of living systems at form, pattern, structure, function, interaction, and evolution every level. through biological data objects. Given a sequence of data such as a DNA or amino acid The understanding of patterns, biology, and sequence, a motif or a pattern is a repeating subsequence. network biology will be of crucial importance to the scientific Such repeated subsequences often have important biological understanding of living systems. However a full significance and hence discovering such motifs in various understanding of a living system requires further biological databases turns out to be a very important problem understanding the system’s pattern, structure, and process. A in . Of course, in biological applications new synthesis of living systems was introduced by Capra [1]. the various occurrences of a pattern in the given sequence may The key idea of his synthesis is to express the key criteria of a not be exact and hence it is important to be able to discover living system in terms of three conceptual dimensions, pattern motifs even in the presence of small errors. Various tools are (autopoiesis), structure (dissipative structure), and process now available for carrying out automatic pattern discovery. (cognition). This is usually the first step towards a more sophisticated task such as gene finding in DNA or secondary structure prediction 3.1. Autopoiesis-The Pattern of Life in protein sequences at system level. Autopoiesis literally means "auto ()-creation" and 2.1 Systems Biology expresses a fundamental interaction between structure and function. The term was originally introduced by Humberto Systems biology is an emergent field that aims at system-level Maturana and in 1973 [7, 8]. According to understanding of biological systems. It focuses on systems Maturana and Varela, a living system continuously produces that are composed of molecular components. This may include itself. Autopoiesis is a network pattern in which the function the understanding of structure of the system such as gene of each component involves with the production or regulatory and biochemical networks and the understanding of transformation of other components in the network. The dynamics of the system both quantitative and qualitative simplest living system we know is the biological cell. The analysis. There are numbers of exciting and profound issues eukaryotic cell, for example, is made of various biochemical that are actively investigated, such as robustness of biological components such as nucleic acids and proteins, and is systems, network structures and dynamics, and applications to organized into bounded structures such as the cell nucleus, drug discovery. Systems biology and network biology are in various organelles, a cell membrane and cytoskeleton. These their infancy, but these are the areas that have to be explored structures, based on an external flow of molecules and energy, and the areas that demonstrate the main stream in biological produce the components which, in turn, continue to maintain sciences in this century [4.5]. the organized bounded structure that gives rise to these components. An autopoietic system is to be contrasted with an 2.2 Biological Networks allopoietic system, such as a car factory, which uses raw materials (components) to generate a car (an organized One of the ultimate goals of biological networks is to improve structure) which is something other than itself (a factory). our understanding of the processes and events that lead to More generally, the term autopoiesis resembles the dynamics and diseases. The analysis of biological pathways of a non-equilibrium system; that is, organized states that can provide a more efficient way of browsing through remain stable for long periods of time despite matter and biologically relevant information, and offer a quick overview energy continually flowing through them. From a very general of underlying biological processes. Protein interactions help point of view, the notion of autopoiesis is often associated put biological processes in context, allowing researchers to with that of self-organization. However, an autopoietic system characterize specific pathway biology. Hence, the analysis of is autonomous and operationally closed, in the that biological networks is crucial for the understanding of every process within it directly helps in maintaining the complex biological systems and diseases. The analysis of whole. Autopoietic systems are structurally coupled with their protein interaction networks is an important and very active medium in a dialect dynamics of changes that can be called research area in bioinformatics and computational biology [2]. sensory-motor coupling. This continuous dynamics is considered as knowledge and can be observed throughout life- forms [6, 9, 11]. Mathematical models of self-organizing III. EMERGING PATTERN, DISSIPATIVE networks were known as cellular automata - a powerful tool STRUCTURE, AND EVOLVING COGNITION for simulating autopoiesis networks. A cellular automaton is a collection of "colored" cells on a grid of specified shape that Patterns, structures, and rules arise and play an important evolves through a number of discrete time steps according to a role in living systems and nearly all . This set of rules based on the states of neighboring cells. The rules is particularly true in mathematics, physics, theoretical are then applied iteratively for as many time steps as desired. biology, and . It is remarkable that aspects of Von Neumann was one of the first people to consider such a pattern discovery have only recently been explored in the field model, and incorporated a cellular model into his "universal of and bioinformatics. There is now a growing constructor." Cellular automata were studied in the early collection of investigations in bioinformatics attempting to 1950’s as a possible model for biological systems [13]. The investigate patterns, structures, and processes at every level of simplest type of cellular automaton is a binary, nearest-

ISBN: 978-960-474-363-6 259 Modern Computer Applications in Science and Education neighbor, one-dimensional automaton. Such automata were studies the structure, function, evolutionary called "elementary cellular automata" by S. Wolfram, who has , development, genetics, biochemistry, physiology, extensively studied their amazing [13]. The theory pharmacology, informatics, computational neuroscience and of cellular automata is immensely rich, with simple rules and of the nervous system. The nervous system is structures capable of producing a great variety of unexpected composed of a network of and other supportive cells behaviors. (functional circuits). Each is responsible for specific tasks to the behaviors at the organism level. Therefore 3.2 Dissipative Structure-the Structure of Living Systems neuroscience can be studied at many different levels, ranging from the molecular level to the cellular level to the systems The term dissipative structure of a living system was coined level to the cognitive level. by Ilya Prigogine who pioneered research in the field of At the molecular level, the basic questions addressed in thermodynamics in [9]. A dissipative structure is a molecular neuroscience include the mechanisms by which thermodynamically open system to the flow of energy and neurons express and respond to molecular signals and how matter. A dissipative structure is operating far from axons form complex connectivity patterns. At this level, tools thermodynamic equilibrium in an environment with which it from molecular biology and genetics are used to understand exchanges energy and matter. Prigogine descries a living how neurons develop and die, and how genetic changes affect system as a dissipative structure and is both structurally open biological functions. yet organizationally close. Matter continually flows through it, At the cellular level, the fundamental questions addressed but the system maintains a stable form, and it does so in cellular neuroscience are the mechanisms of how neurons autonomously through self-organization. Simple examples of process signals physiologically and electrochemically. They dissipative structure include convection, cyclones and address how signals are processed by the dendrites, somas and hurricanes. More complex examples include lasers, Bénard axons, and how neurotransmitters and electrical signals are cells, the Belousov-Zhabotinsky reaction and at the most used to process signals in a neuron. sophisticated level, life itself. The vast network of metabolic At the systems level, the questions addressed in systems processes keeps the system in a state far from equilibrium and neuroscience include how the circuits are formed and used gives rise to bifurcations through its inherent feedback loops. anatomically and physiologically to produce the physiological functions, such as reflexes, sensory integration, motor 3.3 Cognition-the Process of Life coordination, circadian rhythms, emotional responses, learning and memory. In other words, they address how these The of cognition is closely related to such abstract neural circuits function and the mechanisms through which as mind, reasoning, perception, intelligence, behaviors are generated. learning, and many others that describe numerous capabilities At the cognitive level, addresses of the human mind and expected properties of artificial or the questions of how psychological/cognitive functions are synthetic intelligence. Cognition or cognitive processes can be produced by the neural circuitry. The emergence of powerful natural and artificial, conscious and not conscious; therefore, new measurement techniques such as , they are analyzed from different perspectives and in different electrophysiology and human genetic analysis combined with contexts, in neurology, , , and computer sophisticated experimental techniques from cognitive science. Cognition, according to Maturana and Varela, is the psychology allow neuroscientists and psychologists to address activity involved in the self-generation and self-perpetuation abstract questions such as how human cognition and of living systems. In other words, cognition is the very process are mapped to specific neural circuitries. of life. In this new view, cognition involves the entire process of life - including perception, emotion, and behavior - and does not necessarily require a brain and a nervous system. At IV. COGNITIVE COMPUTING the human level, however, cognition includes , conceptual thought, and all the other attributes of human Cognitive informatics studies intelligent behavior and . Mind is not a thing but a process - the process cognition. Cognition includes mental states and processes, of cognition, which is identified with the process of life. The such as thinking, reasoning, learning, perception, emotion, brain is a specific structure through which this process consciousness, remembering, language understanding and operates. The relationship between mind and brain, therefore, generation, etc. In the emerging theory of living systems mind is one between process and structure. The brain, moreover, is is not a thing, but a process. It is cognition, the process of by no means the only structure involved in the process of knowing, and it’s identified with the process of life itself. cognition. In the human organism, as in the organisms of all provided cognitive science with the first model of vertebrates, the immune system is increasingly being cognition - that is, as the manipulation of symbols based on a recognized as a network that is as complex and interconnected set of rules. The main themes of cognitive informatics as the nervous system and serves equally important encompass three categories of topics, i.e., cognitive coordinating functions [1]. computing, computational intelligence, and neural informatics outlined as the Theoretical Framework of Cognitive 3.4 Neuroscience Informatics [12].

ISBN: 978-960-474-363-6 260 Modern Computer Applications in Science and Education

4.1. Denotational Mathematics Recently biologists and philosophers have been attracted by an evolutionary . It was argued that our cognitive Denotational mathematics is a category of expressive abilities are the outcome of organic evolution, and that, mathematical structures that deals with high level conversely, evolution itself may be described as a cognition mathematical entities beyond numbers and sets, such as process. Furthermore, it is argued that the key to an adequate abstract objects, complex relations, behavioral information, evolutionary epistemology lies in a system-theoretical concepts, knowledge, processes, and systems. Denotational approach to evolution which grows from, but goes beyond, mathematics is usually in the form of abstract algebra, a Darwin's theory of natural selection. Although random branch of mathematics in which a system of abstract notations mutation and natural selection are all still acknowledged as is adopted to denote relations of abstract mathematical entities important aspects of biological evolution, the central focus is and their algebraic operations based on given axioms and shifting from evolution to co-evolution. This is an ongoing . dance that proceeds through a subtle interplay of competition Typical paradigms of denotational mathematics are and cooperation, creation and mutual adaptation. In other concept algebra, system algebra, Real-Time Process Algebra words, a proper understanding of human evolution is (RTPA), Visual Semantic Algebra (VSA), fuzzy , and impossible without understanding the evolution of language, rough sets. A wide range of applications of denotational art, and . We must turn our to the mind- mathematics has been identified in many modern science and process of life. engineering disciplines that deal with complex and intricate mathematical entities and structures beyond numbers, Boolean 4.3 Neural Networks variables, and traditional sets [12]. The concept of a neural network appears to have first been Within the new forms of descriptive mathematics, concept proposed by in his 1948 paper "Intelligent algebra is designed to deal with the new abstract Machinery". Historically, evolved from the von mathematical structure of concepts and their representation Neumann architecture, which is based on sequential and manipulation. Concept algebra provides a denotational processing and execution of explicit instructions. On the other mathematical means for algebraic manipulations of abstract hand, the origins of neural networks are based on efforts to concepts. Concept algebra can be used to model, specify, and model in biological systems, which manipulate generic “to be” type problems, particularly system may rely largely on parallel processing as well as implicit architectures, knowledge bases, and detail-level system instructions based on recognition of patterns of 'sensory' input designs, in cognitive informatics, computational intelligence, from external sources. In other words, at its very heart a computing science, software engineering, and knowledge neural network is a complex statistical processor. Artificial engineering. neural networks are made up of interconnecting artificial System algebra is created for the rigorous treatment of neurons. Artificial neural networks may either be used to gain abstract systems and their algebraic operations. System an understanding of biological neural networks, or for solving algebra provides a denotational mathematical means for problems without necessarily creating a algebraic manipulations of all forms of abstract systems. model of a real biological system. Biological neural networks System algebra can be used to model, specify, and manipulate are made up of real biological neurons that are connected or generic “to be” and “to have” type problems, particularly functionally-related in the nervous system or the system architectures and high-level system designs, in central nervous system. A biological neuron may have as cognitive informatics, computational intelligence, computing many as 10,000 different inputs, and may send its output to science, software engineering, and system engineering. many other neurons. A single neuron may be connected to RTPA is developed to deal with a series of behavioral many other neurons and the total number of neurons and processes and architectures of software and intelligent connections in a network may be extensive. In the field of systems. RTPA provides a coherent notation system and a neuroscience, they are often identified as groups of neurons formal engineering methodology for modeling both software that perform a specific physiological function in laboratory and intelligent systems. RTPA can be used to describe both analysis. The cognitive modeling field involves the physical or logical and physical models of systems, where logic views of mathematical modeling of the behavior of neural systems; the architecture of a software system and its operational ranging from the individual neural level such as modeling the platform can be described using the same set of notations. spike response curves of neurons to a stimulus, through the A wide range of applications of denotational mathematics neural cluster level such as modeling the release and effects of has been identified, which encompass concept algebra for dopamine in the basal ganglia to the complete organism on knowledge manipulations, system algebra for system behavioral modeling of the organism's response to stimuli. In architectural manipulations, and RTPA for system behavioral more practical terms neural networks are non-linear statistical manipulations [12]. data modeling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Whilst a detailed description of neural 4.2 Evolving Cognition systems is nebulous, progress is being charted towards a better understanding of basic biological mechanisms.

ISBN: 978-960-474-363-6 261 Modern Computer Applications in Science and Education

4.4 Biological Peptides and Psychosomatic Network [4] Kitano, H.(2002a). Systems Biology: a brief overview, Science, 295:1662-1664. [5] Kitano, H.(2002b). Computational Systems Biology, Nature, 420:206- Research in the 1980’s as described in [1] uncovered 210. ubiquitous neuron-peptide-receptor distribution in brain [6] Luisi, P. L. (1993). Defining the transition to life: Self-replicating structures associated with emotional processing, and bounded structures and chemical autopoiesis. In W. Stein and F. J. throughout many organ systems. This finding supported Varela, editors, Thinking About Biology, volume III of SFI Studies in the Sciences of Complexity, Lecture Notes, pages 3-23. Addison-Wesley. neuron-peptides as biochemical substrates of emotion, and the [7] Maturana, H. and Varela, F. (1980). Biology of Cognition, published neuron-peptide-receptor network as a parasynaptic system originally in 1970; reprinted in Maturana and Varela. crossing traditional brain-body boundaries. The medical [8] Maturana, H. and Varela, F. (1973). Autopoiesis and Cognition: the relevance of these findings was affirmed by psycho- Realization of the Living, Robert S. Cohen and Marx W. Wartofsky (Eds.), Boston Studies in the 42, Dordecht: D. neuroimmunology research. Neuro-peptides help to regulate Reidel Publishing Co. immunocyte trafficking. There is bidirectional communication [9] Mingers, J.(1994). Self-Producing Systems. Kluwer Academic/Plenum between nervous and immune system components, Publishers. immunocytes produce neuron-peptides, and nerve cells [10] Prigogine, I. (1967). Dissipative Structures in Chemical Systems, in Stig Claesson (ed.) Fast Reactions and Primary Processes in Chemical produce immune-associated cytokines. In the past decade, the Kinetics, Interscience, New York. concept of a unified psychosomatic network has been [11] Varela, F., Maturana, H., and Uribe, R. (1974). Autopoiesis: the strengthened by animal and human research demonstrating organization of living systems, its characterization and a model. relationships between behavior and neuron-peptide-mediated Biosystems, 5 187–196. von der Malsburg, C. (1973). Self-organization regulation of immune functions. of orientation sensitive cells in the striate cortex. Kybernetik, 14:85- 100. The discovery of this psychosomatic network implies that [12] Wang, Y., Zhang, D., Tsumoto, S. (2009). Preface: Cognitive the nervous system is not hierarchically structured and Informatics, Cognitive Computing, and Their Denotational ultimately this implies that cognition is a phenomenon that Mathematical Foundations (I) Fundamenta Informaticae, 90, i–vii i DOI expands throughout the organism, operating through an 10.3233/FI-2009-000, IOS Press [13] Wolfram, S. (2002). A New Kind of Science, Champaign, IL: Wolfram intricate chemical network of peptides that integrates our Media. mental, emotional, and biological activities. Matthew He, Ph.D., is a full professor and director of the Division of Mathematics, Science, and Technology of Nova Southeastern University in Florida. He has been a full professor and grand Ph.D. of the World V. CHALLENGES AND PERSPECTIVES Information Distributed University since 2004, as well as an academician of the European of Informatization. He received a Ph.D. in From a scientific perspective discovering how the brain mathematics from the University of South Florida in 1991. He was a research thinks is a major undertaking in the history of mankind. associate at the Department of Mathematics, Eldgenossische Technische Hochschule, Zurich, Switzerland, and the Department of Mathematics and Bioinformatics provides computational and experimental tools Theoretical Physics, Cambridge University, Cambridge, England. He was also to study the biological patterns, structures, and functions. a visiting professor at the National Key Research Lab of Computational Cognitive informatics investigates the internal information Mathematics of the Chinese Academy of Science and the University of Rome, processing mechanisms and process of life-cognition. Italy.

“Understanding the human mind in biological terms has Dr. He has authored and edited eight books and published over 100 research emerged as the central challenge for science in the twenty first papers in the areas of mathematics, bioinformatics, , century. We want to understand the biological nature of , mathematics, and engineering techniques in the medical perception, learning, memory, thought, consciousness, and the and biological sciences. He is an editor-in-chief of International Journal of and Computer Science, a guest editor a special Issue limits of ,” as Kandel put it in [3] “Thus, we gain on Information Engineering and Applied Computing of the Journal of from the new science of mind not only insights into ourselves- Supercomputing, September 2011, Springer, an editor of International Journal how we perceive, learn, remember, feel, and act-but also a of Software Science and Computational Intelligence, International Journal of new perspective of ourselves in the context of biological Cognitive Informatics and Natural Intelligence, International Journal of Biological Systems, and International Journal of Integrative Biology. He is an evolution.” “The task of neural science is to explain behavior invited series editor of biomedical and life sciences in Henry Stewart Talk’s in terms of the activities of the brain. How does the brain “Using Bioinformatics in Exploration in Genetic Diversity.” He received the marshal its millions of individual nerve cells to produce World Academy of Sciences Achievement Award in recognition of his behavior, and how are these cells influenced by the research contributions in the field of computing in 2003 and 2010. He is environment...? The last frontier of the biological sciences – chairman of the International Society of Symmetry in Bioinformatics and a member of International Advisory Board of the International Symmetry their ultimate challenge – is to understand the biological basis Association. He is a member of the American Mathematical Society, the of consciousness and the mental processes by which we Association of Computing Machinery, the IEEE Computer Society, the World perceive, act, learn, and remember.” Association of Science Engineering, and an international advisory board member of the bioinformatics group of the International Federation for Information Processing. He was an international scientific committee co-chair REFERENCES of the International Conference of Bioinformatics and Its Applications in 2004 and a general co-chair of the International Conference of Bioinformatics [1] Capra, F. (1997). The Web of Life. Random House. Research and Applications in 2009, and has been a keynote speaker at many [2] Dyke, C. (1988). The Evolutionary Dynamics of Complex Systems: A international conferences in the areas of mathematics, bioinformatics, and Study in Biosocial Complexity, New York: Oxford University Press. and engineering. [3] Kandel, E. R. (2006). In Search of Memory, the emergence of a new science of mind, W.W. Norton & Company, New York, London.

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