Statistical physics approaches to subnetwork dynamics in biochemical systems
File(s)
Accepted version
Author(s)
Bravi, B
Sollich, P
Type
Journal Article
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.
Date Issued
2017-07-19
Date Acceptance
2017-05-16
Citation
Physical Biology, 2017, 14 (4), pp.1-27
ISSN
1478-3967
Publisher
IOP Publishing
Start Page
1
End Page
27
Journal / Book Title
Physical Biology
Volume
14
Issue
4
Copyright Statement
© 2017 IOP Publishing Ltd. This is an author-created, un-copyedited version of an article accepted for publication in Physical Biology. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at https://doi.org/10.1088/1478-3975/aa7363.
Identifier
https://iopscience.iop.org/article/10.1088/1478-3975/aa7363
Subjects
Biophysics
02 Physical Sciences
06 Biological Sciences
09 Engineering
Publication Status
Published
Date Publish Online
2017-07-19