Statistical physics approaches to subnetwork dynamics in biochemical systems

Physical Biology
B Bravi, Peter Sollich

Abstract

We apply a Gaussian variational approximation to model reduction in large biochemical networks of unary and binary reactions. We focus on a small subset of variables (subnetwork) of interest, e.g. because they are accessible experimentally, embedded in a larger network (bulk). The key goal is to write dynamical equations reduced to the subnetwork but still retaining the effects of the bulk. As a result, the subnetwork-reduced dynamics contains a memory term and an extrinsic noise term with non-trivial temporal correlations. We first derive expressions for this memory and noise in the linearized (Gaussian) dynamics and then use a perturbative power expansion to obtain first order nonlinear corrections. For the case of vanishing intrinsic noise, our description is explicitly shown to be equivalent to projection methods up to quadratic terms, but it is applicable also in the presence of stochastic fluctuations in the original dynamics. An example from the epidermal growth factor receptor signalling pathway is provided to probe the increased prediction accuracy and computational efficiency of our method.

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Citations

Dec 30, 2017·Physical Review Letters·Matteo Polettini, Massimiliano Esposito
Oct 23, 2018·PLoS Computational Biology·Braden A W BrinkmanMichael A Buice
May 20, 2018·Physical Review. E·Pavel Chvykov, Jeremy England
Jul 17, 2020·The Journal of Chemical Physics·Barbara BraviPeter Sollich
Jan 8, 2018·The Journal of Chemical Physics·Leo Bronstein, Heinz Koeppl
Jul 18, 2018·Physical Review. E·Leo Bronstein, Heinz Koeppl
May 20, 2020·Physical Review. E·Jonas StapmannsMoritz Helias

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