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Dynamical mean-field theory: from ecosystems to reaction networks

Published version
Peer-reviewed

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Abstract

jats:titleAbstract</jats:title> jats:pBoth natural ecosystems and biochemical reaction networks involve populations of heterogeneous agents whose cooperative and competitive interactions lead to a rich dynamics of species’ abundances, albeit at vastly different scales. The maintenance of diversity in large ecosystems is a longstanding puzzle, towards which recent progress has been made by the derivation of dynamical mean-field theories of random models. In particular, it has recently been shown that these random models have a chaotic phase in which abundances display wild fluctuations. When modest spatial structure is included, these fluctuations are stabilized and diversity is maintained. If and how these phenomena have parallels in biochemical reaction networks is currently unknown. Making this connection is of interest since life requires cooperation among a large number of molecular species. In this work, we find a reaction network whose large-scale behavior recovers the random Lotka–Volterra model recently considered in theoretical ecology. We clarify the assumptions necessary to derive its large-scale description, and reveal the underlying assumptions made on the noise to recover previous dynamical mean-field theories. Then, we show how local detailed balance and the positivity of reaction rates, which are key physical requirements of chemical reaction networks, provide obstructions towards the construction of an associated dynamical mean-field theory of biochemical reaction networks. Finally, we outline prospects and challenges for the future.</jats:p>

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Keywords

Paper, Random Landscapes and Dynamics in Evolution, Ecology and Beyond, disordered systems, statistical field theory, theoretical ecology, reaction networks

Journal Title

Journal of Physics A: Mathematical and Theoretical

Conference Name

Journal ISSN

1751-8113
1751-8121

Volume Title

Publisher

IOP Publishing
Sponsorship
Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (Discovery Grant RGPIN-2020-04762)
Sidney Sussex College, University of Cambridge (Ramon Jenkins Research Fellowship)
University of Cambridge (Herchel Smith Fellowship)