Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks

We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g. protein interaction networks involving complex formation and dissociation reactions. We propose the use of model reduction strategies to understand the 'extrinsic' source...

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Bibliographic Details
Published in:arXiv.org
Main Authors: Bravi, Barbara, Rubin, Katy J, Sollich, Peter
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 01.07.2020
Subjects:
Biochemistry
Complex formation
Growth factors
Model reduction
Noise
Noise propagation
Proteins
ISSN:2331-8422
Online Access:Get full text
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Summary:We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g. protein interaction networks involving complex formation and dissociation reactions. We propose the use of model reduction strategies to understand the 'extrinsic' sources of stochasticity arising from the rest of the network. Our approaches are based on subnetwork dynamical equations derived by projection methods and by path integrals. The results provide a principled derivation of the different components of the extrinsic noise that is observed experimentally in cellular biochemical reactions, over and above the intrinsic noise from the stochasticity of biochemical events in the subnetwork. We explore several intermediate approximations to assess systematically the relative importance of different extrinsic noise components, including initial transients, long-time plateaus, temporal correlations, multiplicative noise terms and nonlinear noise propagation. The best approximations achieve excellent accuracy in quantitative tests on a simple protein network and on the epidermal growth factor receptor signalling network.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2003.08704