Identifying Dynamical Bottlenecks of Stochastic Transitions in Biochemical Networks
Name
Govern-2012-Identifying Dynamical Bottlenecks of Stochastic Transitions in Biochemical Networks.pdf
Size
167.51 KB
Format
Adobe PDF
Checksum (MD5)
a306f78c7cdb0a43f6872dd7c09556bc
Author(s)
Yang, Ming
Chakraborty, Arup K.
Govern, Christopher C.
Date Issued
January 2012
Journal
Physical Review Letters
Publisher
American Physical Society
Citation
Govern, Christopher, Ming Yang, and Arup Chakraborty. “Identifying Dynamical Bottlenecks of Stochastic Transitions in Biochemical Networks.” Physical Review Letters 108.5 (2012): Web. 17 May 2012. © 2012 American Physical Society
Version
Final published version
Abstract
In biochemical networks, identifying key proteins and protein-protein reactions that regulate fluctuation-driven transitions leading to pathological cellular function is an important challenge. Using large deviation theory, we develop a semianalytical method to determine how changes in protein expression and rate parameters of protein-protein reactions influence the rate of such transitions. Our formulas agree well with computationally costly direct simulations and are consistent with experiments. Our approach reveals qualitative features of key reactions that regulate stochastic transitions.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Ragon Institute of MGH, MIT and Harvard
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/PhysRevLett.108.058102
