Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks.
File(s)
Published version
Author(s)
Bravi, Barbara
Rubin, Katy J
Sollich, Peter
Type
Journal Article
Abstract
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 path integrals. The results provide a principled derivation of 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 signaling network.
Date Issued
2020-07-14
Date Acceptance
2020-06-22
Citation
Journal of Chemical Physics, 2020, 153 (2), pp.1-20
ISSN
0021-9606
Publisher
American Institute of Physics
Start Page
1
End Page
20
Journal / Book Title
Journal of Chemical Physics
Volume
153
Issue
2
Copyright Statement
© 2020 Author(s).
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/32668933
Subjects
ErbB Receptors
Models, Biological
Protein Interaction Maps
Stochastic Processes
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2021-07-09