Elastic Multi-scale Mechanisms: Computation and Biological Evolution

Original Article

Abstract

Explanations based on low-level interacting elements are valuable and powerful since they contribute to identify the key mechanisms of biological functions. However, many dynamic systems based on low-level interacting elements with unambiguous, finite, and complete information of initial states generate future states that cannot be predicted, implying an increase of complexity and open-ended evolution. Such systems are like Turing machines, that overlap with dynamical systems that cannot halt. We argue that organisms find halting conditions by distorting these mechanisms, creating conditions for a constant creativity that drives evolution. We introduce a modulus of elasticity to measure the changes in these mechanisms in response to changes in the computed environment. We test this concept in a population of predators and predated cells with chemotactic mechanisms and demonstrate how the selection of a given mechanism depends on the entire population. We finally explore this concept in different frameworks and postulate that the identification of predictive mechanisms is only successful with small elasticity modulus.

Keywords

Systems biology Elastic mechanisms Computational theory Turing machines Evolution Open-ended evolution 

Notes

Acknowledgements

I want to acknowledge the constructive input from two anonymous referees who helped me to refine and set this work on a solid basis. Without this collaborative work with the reviewers it hasn’t been possible to give the final form of this manuscript. I am also grateful to Elena Ramírez for decisive discussions that provided the fundamental elements exposed in this manuscript.

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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Data Science & Machine Learning DivisionPerMediQ GmbHWangGermany

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