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5. 1- What is Life?

Biologically-inspired computing is an interdisciplinary field that formalizes processes observed in living systems to design computational methods for solving complex problems, or simply to endow artificial systems with more natural traits. But to draw more than superficial inspiration from Biology we need to understand and discuss the concept of life. It should be noted that for the most part of the history of humanity, the question of what life is was not an important issue. Before the study of mechanics became important, everything was thought to be alive: the stars, the skies, the rivers and mountains, etc. There was no non-life, so the concept was of no importance. It was only when people started to see the World as determined by the laws of mechanics that the question arose. If all matter follows simple physical laws, then what is indeed the difference between life and non-life, between biology and physics? Let us then start with a current dictionary definition:

The definitions above fall into three main categories: (1) life as an organization distinct from inorganic matter (with an associated list of properties), (2) life as a certain kind of animated behavior, and (3) life as a special, incommensurable, quality–vitalism. Throughout this course we will see that all principles, and indeed all controversies, associated with the study of life fall into one of these categories or the differences among them. The third category has been discarded as a viable scientific explanation, because for science nothing is in principle incommensurable. The question of whether life is organized according to a special design, intelligent or mysterious, pertains to metaphysics. If the agent of design cannot be observed with physical means, then it is by definition beyond the scope of science as it cannot be measured, and any theories derived from such a concept cannot tested.

While metaphysical dispositions do not pertain to science, systems thinking has lead many scientists to observed that a naive mechanistic decomposition of life may also fail to explain it [Klir, 2001]. The traditional scientific approach has lead the study of living systems into a reductionist search for answers in the nitty-gritty of the biochemistry of living organisms. In this approach, life is nothing more than the complicated physics of a collection of moving bodies. However, the question remains unanswered since there are many ways to obtain some complicated dynamics, but of all of these, which ones can be classified as alive? What kind of complexity are we looking for? No one disputes that life is some sort of complex material arrangement, but when does an organization reach a necessary threshold of complexity after which matter is said to be living? Is it a discrete step, or is life a fuzzy concept? Must such an organization have a material implementation (possess a thinghood), or can it be completely abstracted (fully explained by its systemhood) [Rosen, 1986]? To understand it without meaningless reduction, must we synthesize organizations with the same threshold of complexity (first category above), or is it enough to simulate its animated behavior (second category above) [Pattee, 1989]?

Traditionally life has been identified with material organizations which observe certain lists of properties, e.g. metabolism, adaptability, self-maintenance (autonomy), self-repair, growth, replication, evolution, etc. Most living organisms follow these lists, however, there are other material systems which obey only a subset of these rules, e.g. viruses, candle flames, the Earth, certain robots, etc. This often leads to the view that life is at best a fuzzy concept and at worst something we are, subjectively, trained to recognize–life is what we can eat–and is thus not an objective distinction. The modern-day molecular biology view of life, on the other hand, tends to see life as a material organization that if not completely programmed by genomic information, is at least very controlled by it [Brenner, 2012, Cobb, 2013]. Thus, when Craig Venter's team [Gibson et al, 2010] produced bacteria with a "prosthetic genome" [a termed coined by Mark Bedau, see Nature | Opinion, 2010] copied from other bacteria but synthesized in the lab, the momentous synthetic biology feat was announced as the creation of the first synthetic or artificial life form.

Indeed, it is remarkable that in Venter's experiment, a cell with a synthesized prosthetic genome from a similar but distinct organism, was able to reproduce over and over resulting in a cell with a different phenotype from the original, implanted cell---in effect, a cell re-programmed by a synthesized genome. Is life then a type of computer that can be reprogrammed [Brenner, 2012]? This also leads us to question how general-purpose can such genomic re-programming be? Will it be restricted to very narrow classes of similar organisms, or will it ever be possible to re-program any prokaryotic or eukaryotic cell ?

The artificial life field, whose members tend to follow the fuzzy list of properties conception of life, does not typically recognize Venter's bacteria with a prosthetic genome as a bona fide synthesis of artificial life, since it relies on the pre-existence of a working, naturally-obtained cell to implant a prosthetic genome into. Even most molecular biologists will agree that we are nowhere near understanding, let alone synthesizing an artificial cell from scratch [e.g. George Church, see Nature | Opinion, 2010]. Nonetheless, Venter's achievement begs at least the question of what is it about life's design principle that makes it easier to synthesize a working prosthetic genome than a working "prosthetic proteome or metabolome"? It also makes us think about what does "understanding life" mean for biology, biomedical complexity, artificial life, and computation? Why is genetic information so important and how does it relate to information technology?

Life requires the ability to both categorize and control events in its environment in order to survive. In other words, organisms pursue (or even decide upon) different actions according to information they perceive in an environment. Furthermore, living organisms reproduce and develop from genetic information. More specifically, genetic information is transmitted "vertically" (inherited) in phylogeny and cell reproduction, and expressed "horizontally" within a cell in ontogeny and plain functioning of living organisms as they interact and react with their environments–we are now sure that genetic information can also be transmitted horizontally between organisms and play an important role in evolution [Goldenfeld & Woese 2007; Riley, 2013]. Indeed, the difference between living and non-living organizations seems to stand on the ability of the former to use relevant information for their own functioning. It is this "relevant" which gives life an extra attribute to simple mechanistic interactions. When an organization is able to recognize and act on aspects of its environment which are important to its own survival, we say that the mechanisms by which the organization recognizes and acts are functional in reference to the organization itself (self-reference).

We should note that Physics is not concerned with function [Pattee, 1978]. A physical or chemical description of DNA is certainly possible, but will tell us nothing as to the function of a DNA molecule as a gene containing relevant information for a particular organism–in this case, information derives from the sequence of four possible nucleotides not from their minute chemical details [Eigen, 1992]. Only in reference to an organism in a particular context does a piece of DNA function as a gene (e.g. an enzyme with some biochemical effect in an organism in an environment). The effective separation of the material from the functional and organizational is very much a systems concept, leading many to study alternative material arrangements and general organizations that can sustain life.

The issue of function and reference in the living organization is often approached by appealing to the notion of emergence or collective behavior. Whatever (macro-level) organization exists after the complexity threshold for life is passed, we may say that it is emergent because its attributes cannot be completely explained by the (micro-) physical level. Function, control, and categorization cannot be explained solely by the mechanics and dynamics of the components (individual variables) of life . Understanding and predicting life, requires the study of the collective behavior that emerges from complex multivariate, intra- and inter-organism exchanges in eco-social (and even technological) multi-level networks. Notice, however, that emergence does not imply vitalism or dualism. When we say that certain characteristics of life cannot be explained by physics alone, we mean that they must be explained by different, additional models---namely, informational, historical and functional descriptions. In other words, though biological function, control, and categorization cannot be explained by physics alone, organisms, like anything else, must nonetheless follow physical laws. But information is contextual and historical, and therefore requires more than universal models: it requires contingent, context-specific descriptions. For instance, the INS gene encodes instructions to produce insulin in humans (and a few other species, with orthologs in hundreds of others). But the same gene is meaningless for the biochemistry of other species, even allowing us to use bacteria as (syntactic) factories of human insulin.

The origin of life, is thus a problem of emergence of contextual information from a physical milieu (of universal laws) under specific constraints [Eigen, 1992]. This is the crux of complex systems: the interplay between micro- and macro-level constraints determines their behavior, and multiple, non-decomposable, complementary levels (emergence) are required to understand biological (and biomedical) complexity [Pattee, 1978]. The definition of emergence as an epistemological, explanatory requirement, is related to the notion of emergence-relative-to-a-model [Rosen, 1985; Cariani, 1989] or intensional emergence [Salthe 1991]. It refers to the impossibility of epistemological reduction of the properties of a system to its components [Clark, 1996]. As an example, we can think of phase transitions such as that of water in its transition from liquid to gas. Water and its properties cannot be rephrased it terms of the properties of hydrogen and oxygen, it needs a qualitatively different model. Another example of complementary models of the same material systems is the wave-particle duality of light.

Physicists understand the laws of nature (as best they can), but it takes engineers to control nature. The very best physicists are the very best engineers, but those are arguably rare (e.g. Von Neumann). The goal of complex systems science is to understand organized complexity (life, society, cognition) in the same way physicists understand nature [Weaver, 1948]. Biology, as a discipline has not entirely "made up its mind" if it wants to understand life as a physicist or control it as an engineer. Due to its focus on the micro-level of life, its biochemistry, molecular biology follows essentially a (reverse-) engineering, black-box methodology (knockouts, controls, etc.) to understand mechanism. This leads to a bit of a schizophrenic agenda: focusing exclusively on micro-level experiments in order to suggest macro-level understandings. . If the goal is control of biology, say for biomedical advances, then focus should turn to biotechnology engineering–synthetic biology is a good example of this focus shift. If the goal is understanding, then focus should be more on macro-level organized complexity. Ideally, a healthy life sciences program would tie the need to understand complexity with the need to control mechanisms–like physicists and engineers do.

This is where complex systems, artificial life, and bio-inspired computing can contribute to a wider arena of the life sciences; they can be used as laboratories for experimenting with theories of organized complexity, and thus enrich our understanding of life. Artificial life concerns both the simulation and realization of life in some artificial environment, usually the computer. At least regarding the second of its goals, artificial life aims to understand the fundamental micro/macro-level interaction that leads to organized complexity. Bio-inspired computing, as a more pragmatic endeavor, does not need to concern itself with synthesizing actual life, but only with drawing analogies from life (real and artificial). Nonetheless, if the main motivation of bio-inspired computing is that life with its designs has already solved versions of many complex engineering problems we are interested in, then a thorough and accurate understanding of the essential characteristics of life is inescapable. Moreover, by abstracting context-specific principles of life to make them relevant in other settings, provides a useful laboratory for theoretical biology.

Brenner, S. [2012]. "Turing centenary: Life's code script." Nature 482 (7386): 461-461.

Cariani, Peter [1989].On the Design of Devices with Emergent Semantic Functions. PhD.Dissertation. SUNY Binghamton.

Clark, Andy [1996]. "Happy couplings: emergence and explanatory interlock." In: The Philosophy of Artificial Life. M. Boden (ed.). Oxford University Press, pp. 262-281.

Cobb, Matthew. [2013]. "1953: When Genes Became 'Information'." Cell 153 (3): 503-506.

Dobzhansky, T. [1973]. "Nothing in Biology Makes Sense Except in the Light of Evolution". The American Biology Teacher, March 1973 (35:125-129)

Eigen, M. [1992]. Steps Towards Life. Oxford University Press.

D. G. Gibson et al [2010]."Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome" Science. 329 (5987): 52-56

Goldenfeld, Nigel, and Carl Woese [2007]. "Biology's Next Revolution." Nature 445 (7126): 369.

Gould, Stephen Jay [1984]. Natural History; Jan84, Vol. 93 Issue 1, p24.

Klir, G. [2001], Facets of Systems Science. Springer.

Mann, T. [1924]. The Magic Mountain. As quoted by Eigen [1990]

Nature | Opinion [2010] "Life after the synthetic cell". Nature 465: 422-424

Pattee, Howard H. [1978]."The complementarity principle in biological and social structures." In: Journal of Social and Biological Structures Vol. 1, pp. 191-200.

Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77.

Polanyi, M. [1968]. "Life's irreducible structure". Science, 160 (3834), 1308-1312.

Riley, D. R., K.B. Sieber, K. M. Robinson, J. R. White, A. Ganesan, S. Nourbakhsh, and J. C. Dunning Hotopp [2013]. "Bacteria-Human Somatic Cell Lateral Gene Transfer Is Enriched in Cancer Samples." PLoS Computational Biology 9 (6): e1003107.

Rosen, R. [1986], "Some Comments on Systems and System Theory". Int. Journal of General Systems. 13 (1).

Salthe, Stanley N. [1991], "Varieties of Emergence". World Futures Vol. 32, pp.69-83

Schrodinger, Erwin [1944]. What is Life?. Cambridge University Press.

Weaver, W. [1948]. "Science and Complexity". American Scientist, 36(4): 536-44.

for the next lectures read

Dennet, D.C. [2005]. "Show me the Science". New York Times, August 28, 2005

Polt, R. [2012]. "Anything but Human". New York Times, August 5, 2012.

Optional for SSIE 483X

Langton, C. [1989], "Artificial Life" In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 1-47.

Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77.

Optional

Aleksander, I. [2002]. "Understanding Information Bit by Bit". In: It must be beautiful : great equations of modern science. G. Farmelo (Ed.), Grant.

Cobb, Matthew. [2013]. "1953: When Genes Became 'Information'." Cell 153 (3): 503-506.

Gleick, J. [2011]. The Information: A History, a Theory, a Flood. Random House. Chapter 8.

James, R., and Crutchfield, J. (2017). Multivariate Dependence beyond Shannon Information. Entropy, 19(10), 531.

Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 1, pp. 1-23.

Prokopenko, Mikhail, Fabio Boschetti, and Alex J. Ryan. "An information theoretic primer on complexity, self organization, and emergence." Complexity 15.1 (2009): 11-28.

Last Modified: Jan 15, 2024

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