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The Many Facets of Natural Computing review articles DOI:10.1145/1400181.1400200 applications, as well as biology, chem- Natural computing builds a bridge between istry, and physics experimental labora- tory research. computer science and natural sciences. In this review we describe com- puting paradigms abstracted from BY LILA KARI AND GRZEGORZ ROZENBERG natural phenomena as diverse as self-reproduction, the functioning of the brain, Darwinian evolution, group behavior, the immune system, the char- acteristics of life, cell membranes, and The Many morphogenesis. These paradigms can be implemented either on traditional electronic hardware or on alternative physical media such as biomolecular Facets of (DNA, RNA) computing, or trapped-ion quantum computing devices. Dually, we describe several natural processes that can be viewed as information pro- Natural cessing, such as gene regulatory net- works, protein-protein interaction net- works, biological transport networks, and gene assembly in unicellular or- Computing ganisms. In the same vein, we list ef- forts to understand biological systems by engineering semi-synthetic organ- isms, and to understand the universe from the point of view of information processing. This review was written with the ex- “Biology and computer science—life and pectation that the reader is a computer scientist with limited knowledge of computation—are related. I am confident that natural sciences, and it avoids dwell- at their interface great discoveries await those ing on the minute details of various natural phenomena. Thus, rather than who seek them.” being overwhelmed by particulars, it is — Leonard Adleman, our hope that readers see this article Scientific American, Aug. 1998 as simply a window into the profound relationship that exists between nature and computation. Natural computing is the field of research that There is information processing in investigates models and computational techniques nature, and the natural sciences are al- ready adapting by incorporating tools inspired by nature and, dually, attempts to under- and concepts from computer science stand the world around us in terms of information at a rapid pace. Conversely, a closer look at nature from the point of view processing. It is a highly interdisciplinary field that of information processing can and will connects the natural sciences with computing science, both at the level of information technology The vivid images peppered throughout this 33 story offer glimpses of what can happen when and at the level of fundamental research. nature, art, and computer science join forces. While not directly referenced in this article, As a matter of fact, natural computing areas and these images serve to offer readers some topics come in many flavors, including pure startling perspectives of nature up close as theoretical research, algorithms and software only technology can provide. 72 COMMUNICATIONS OF THE ACM | OCTOBER 2008 | VOL. 51 | NO. 10 Neri Oxman, an architect and researcher currently working for her Ph.D. in design and computation at MIT, formed an interdisciplinary research initiative called Materialecology that undertakes design research in the intersection between architecture, engineering, computation, biology and ecology. Here, she illustrates how plants often grow in fashion to maximize the surface area of their branching geometries while maintaining structural support. This work was done in collaboration with W. Craig Carter, a professor in MIT’s Department of Material Science and Engineering. For more images, see http://www. materialecology.com/. change what we mean by computation. John von Neumann, who was trained ternative explanation to the phenome- Our invitation to you, fellow computer in both mathematics and chemistry, non of emergence of complexity in the scientists, is to take part in the uncov- investigated cellular automata as a natural world, and used, among others, ering of this wondrous connection.a framework for the understanding of for modeling in physics and biology. the behavior of complex systems. In In parallel to early comparisons39 Nature as Inspiration particular, he believed that self-repro- between computing machines and the Among the oldest examples of nature- duction was a feature essential to both human nervous system, McCulloch and inspired models of computation are biological organisms and computers.40 Pitts proposed the first model of artifi- the cellular automata conceived by A cellular automaton is a dynami- cial neurons. This research eventually Ulam and von Neumann in the 1940s. cal system consisting of a regular grid gave rise to the field of neural computa- of cells, in which space and time are tion, and it also had a profound influ- a A few words are in order about the organization discrete. Each of the cells can be in one ence on the foundations of automata of this article. The classifications and labels of a finite number of states. Each cell theory. The goal of neural computa- we use for various fields of research are purely changes its state according to a list of tion was twofold. On one hand, it was for the purpose of organizing the discourse. In reality, far from being clear-cut, many of the given transition rules that determine hoped that it would help unravel the fields of research mentioned here overlap, or its future state, based on its current structure of computation in nervous fit under more than one category. The general state and the current states of some of systems of living organisms (How does audience for whom this article is intended, our its neighbors. The entire grid of cells the brain work?). On the other hand, it respective fields of expertise, and especially updates its configuration synchro- was predicted that, by using the princi- the limited space available for this review af- fected both the depth and breadth of our expo- nously according to the a priori given ples of how the human brain process- sition. In particular, we did not discuss some transition rules. es information, neural computation fields of research that have large overlaps with Cellular automata have been ap- would yield significant computational natural computing, such as bioinformatics, plied to the study of phenomena as advances (How can we build an intel- computational molecular biology, and their diverse as communication, computa- ligent computer?). The first goal has roles in, for example, genomics and proteom- ics. In addition, our explanations of various tion, construction, growth, reproduc- been pursued mainly within the neu- aspects, themes, and paradigms had to be tion, competition, and evolution. One rosciences under the name of brain necessarily oversimplified. As well, the space of the best known examples of cellular theory or computational neuroscience, we devoted to various fields and topics was automata—the “game of life” invented while the quest for the second goal has influenced by several factors and, as such, has no relation to the respective importance of the by Conway—was shown to be compu- become mainly a computer science field or the relative size of the body of research tationally universal. Cellular automata discipline known as artificial neural in that field. have been extensively studied as an al- networks or simply neural networks.5 OCTOBER 2008 | VOL. 51 | NO. 10 | COMMUNICATIONS OF THE ACM 73 review articles An artificial neural network consists While Turing and von Neumann environmental selection. of interconnected artificial neurons.31 dreamed of understanding the brain, Evolutionary systems have first been Modeled after the natural neurons, and possibly designing an intelligent viewed as optimization processes in the each artificial neuron A has n real-val- computer that works like the brain, evo- 1930s. The basic idea of viewing evolu- 6 ued inputs, x1, x2, …, xn, and it computes lutionary computation emerged as an- tion as a computational process gained its own primitive function fA as follows. other computation paradigm that drew momentum in the 1960s, and evolved Usually, the inputs have associated its inspiration from a completely dif- along three main branches.13 Evolution weights, w1, w2, …, wn. Upon receiving ferent part of biology: Darwinian evolu- strategies use evolutionary processes the n inputs, the artificial neuron A tion. Rather than emulating features of to solve parameter optimization prob- produces the output fA(w1x1 + w2x2 + … a single biological organism, evolution- lems, and are today used for real-val- + wnxn). An artificial neural network is ary computation draws its inspiration ued as well as discrete and mixed types a network of such neurons, and thus from the dynamics of an entire species of parameters. Evolutionary program- a network of their respective primitive of organisms. An artificial evolution- ming originally aimed at achieving the functions. Some neurons are selected to ary system is a computational system goals of artificial intelligence via evo- be the output neurons, and the network based on the notion of simulated evo- lutionary techniques, namely by evolv- function is a vectorial function that, for lution. It features a constant- or vari- ing populations of intelligent agents n input values, associates the outputs of able-size population of individuals, a modeled, for example, as finite-state the m output neurons. Note that differ- fitness criterion according to which the machines. Today, these algorithms ent selections of the weights produce individuals of the population are being are also often used for real-valued pa- rameter optimization problems. Ge- netic algorithms originally featured a From Archimorph, population of individuals encoded as where work is fixed-length bit strings, wherein muta- continuing on their L-System and tions consisted of bit-flips according Evolutionary to a typically small, uniform mutation Algorithm, including rate, the recombination of two parents new images of L-Systems growths consisted of a cut-and-paste of a prefix as well as diagrams of one parent with a suffix of the other, explaining the process and the fitness function was problem- of the overall design. dependent. If the initial individuals For more images, see archimorph. were to encode possible solutions to wordpress.com/.
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