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Convergent evolution in silico of biochemical log-response

Abstract : Numerous biological systems are known to harbour a form of logarithmic behaviour, from Weber's law to bacterial chemotaxis. Working on a logarithmic scale allows the organism to respond appropriately to large variations in a given input at a modest cost in terms of metabolism. Here we use a genetic algorithm to evolve biochemical networks displaying a direct logarithmic response. Interestingly, a quasi-perfect log-response implemented by the same simple core network evolves in a convergent way across our different replications. The best network is able to fit a logarithm over 4 order of magnitude with an accuracy of the order of 1%. At the heart of this network, we show that a logarithmic approximation may be implemented with one single non-linear interaction, that can be interpreted either as a phosphorylation or as a ligand induced multimerization and provide an analytical explanation of the effect. Biological log-response might thus be easier to implement than usually assumed.
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Contributor : Mathieu Hemery Connect in order to contact the contributor
Submitted on : Monday, December 2, 2019 - 2:28:34 PM
Last modification on : Friday, February 4, 2022 - 3:09:19 AM
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  • HAL Id : hal-02389284, version 1


Mathieu Hemery, Paul François. Convergent evolution in silico of biochemical log-response. Journal of Chemical Physics, American Institute of Physics, 2019. ⟨hal-02389284⟩



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