THE CONCEPT OF COMPLEXITY IN EVOLUTIVE BIOLOGY: A SYNTHETIC REVIEW. (original) (raw)
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The “magic” word complexity evokes a multitude of meanings that obscure its real sense. Here we try and generate a bottom-up reconstruction of the deep sense of complexity by looking at the convergence of different features shared by complex systems. We specifically focus on complexity in biology but stressing the similarities with analogous features encountered in inanimate and artefactual systems in order to track an integrative path toward a new “mainstream” of science overcoming the actual fragmentation of scientific culture.
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The overall information processing capacity of an organism is proposed as a conceptual criterion for its complexity. The apparent tendency of complexity increase during the course of evolution is accounted for in terms of positive feedback mechanisms. Furthermore, the different modes of evolution of biological complexity are identified as: The ongoing appearance of more complex species in case of abundance of resources, pausing of the complexity increase when the limits of the resources are reached in a relatively isolated environment, and the extinction of some of the complex species due to lack of sufficient resources. All arguments concerning the definition of complexity and its non-decreasing character are based on concepts like information processing, maintenance of organisation and the related energy expenditures. As a result of these arguments it is concluded that complex adaptations have a teleonomic nature.
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Contemporary scientific knowledge is built on both methodological and epistemological reductionism. The discovery of the limitations of the reductionist paradigm in the mathematical treatment of certain physical phenomena originated the notion of complexity, both as a pattern and process. After clarifying some very general terms and ideas on biological evolution and biological complexity, the article will tackle to seek to summarize the debate on biological complexity and discuss the difference between complexities of living and inert matter. Some examples of the major successes of mathematics applied to biological problems will follow; the notion of an intrinsic limitation in the application of mathematics to biological complexity as a global, relational, and historical phenomenon at the individual and species level will also be advanced.
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A long-standing debate on the causality of levels in biological explanations has divided philosophers into two camps. The reductionist camp insists on the causal primacy of lower, molecular levels, while the critics point out the inescapable shifting, reciprocity, and circularity of levels across biological explanations. We argue, however, that many explanations in biology do not exclusively draw their explanatory power from detailed insights into inter-level interactions; they predominantly require identifying the adequate levels of biological complexity to be explained. Moreover, the main explanatory strategies grounding both theoretical and experimental approaches to one of the central debates in contemporary biology, i.e., on the origin of life, are primarily and sometimes exclusively driven by issues concerning the levels of biochemical complexity, and these only subsequently frame more substantial and detailed accounts of inter-level biochemical interactions.
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In business administration or in economics it is absolutely relevant not to consider indexes like profit growth rate or gross domestic product as exhaustive indexes for economic wealth. Likewise, in biology it is important not to confuse the representation of life with life itself. The most important concepts in biology are information, memory, structure, plasticity, and robustness. Information is the difference that makes the difference. Memories are information registered in an organism. Plasticity is the capacity of a living organism to change its own structure/sets of behaviors for having a competitive advantage. Robustness is the ability of a living organism to resist environmental changes without changing its own structure/sets of behavior, and fragility is when a living organism undergoes changes in its own structure without being able to resist non-adaptive changes.
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Through extensive studies of dynamical system modeling cellular growth and reproduction, we find evidence that complexity arises in multicellular organisms naturally through evolution. Without any elaborate control mechanism, these systems can exhibit complex pattern formation with spontaneous cell differentiation. Such systems employ a "cooperative" use of resources and maintain a larger growth speed than simple cell systems, which exist in a homogeneous state and behave "selfishly." The relevance of the diversity of chemicals and reaction dynamics to the growth of a multicellular organism is demonstrated. Chaotic biochemical dynamics are found to provide the multipotency of stem cells.
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Advances in molecular biology now permit complex biological systems to be tracked at an exquisite level of detail. The information flow is so great, however, that using intuition alone to draw connections is unrealistic. Thus, the need to integrate mathematical biology with experimental biology is greater than ever. To achieve this integration, obstacles that have traditionally prevented effective communication between theoreticians and experimentalists must be overcome, so that experimentalists learn the language of mathematics and dynamical modeling and theorists learn the language of biology. Fifty years ago Alan Hodgkin and Andrew Huxley published their quantitative model of the nerve action potential; in the same year, Alan Turing published his work on pattern formation in activator-inhibitor systems. These classic studies illustrate two ends of the spectrum in mathematical biology: the detailed model approach and the minimal model approach. When combined, they are highly syner...