Structure of deviations from optimality in biological systems

Abstract

Optimization theory has been used to analyze evolutionary adaptation. This theory has explained many features of biological systems, from the genetic code to animal behavior. However, these systems show important deviations from optimality. Typically, these deviations are large in some particular components of the system, whereas others seem to be almost optimal. Deviations from optimality may be due to many factors in evolution, including stochastic effects and finite time, that may not allow the system to reach the ideal optimum. However, we still expect the system to have a higher probability of reaching a state with a higher value of the proposed indirect measure of fitness. In systems of many components, this implies that the largest deviations are expected in those components with less impact on the indirect measure of fitness. Here, we show that this simple probabilistic rule explains deviations from optimality in two very different biological systems. In Caenorhabditis elegans, this rule successfully explains the experimental deviations of the position of neurons from the configuration of minimal wiring cost. In Escherichia coli, the probabilistic rule correctly obtains the structure of the experimental deviations of metabolic fluxes from the configuration that maximizes biomass production. This approach is proposed to explain or predict more data than optimization theory while using no extra parameters. Thus, it can also be used to find and refine hypotheses about which constraints have shaped biological structures in evolution.

Robot Collection and Transport of Objects: A Biomimetic Process

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Synthetic approaches to understanding biological constraints

“…Optimization targets all levels of biological organization at once, from elementary biomolecules to large anatomical features, from the expression levels of single genes to complex individual and social behaviors. The complex interplay of physical, chemical, biological and ecological constraints does not guarantee that absolute optima are achieved by the evolutionary process, and makes the design of optimal organisms hard to predict from first principles [1, 2, 3]. The evolutionary dynamics itself introduces further complications: for instance, finite populations can accumulate deleterious mutations [4], and species which are not the fittest may nevertheless prevail if they are more tolerant to mutations (survival of the flattest vs. survival of the fittest) [5].…”

Dynamic choices are most accurate in small groups

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