Identifying Dynamical Bottlenecks of Stochastic Transitions in Biochemical Networks

Christopher C. Govern, Ming Yang, and Arup K. Chakraborty
Phys. Rev. Lett. 108, 058102 – Published 30 January 2012
PDFHTMLExport Citation

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.

  • Figure
  • Received 6 October 2011

DOI:https://doi.org/10.1103/PhysRevLett.108.058102

© 2012 American Physical Society

Authors & Affiliations

Christopher C. Govern1, Ming Yang1, and Arup K. Chakraborty2,3,*

  • 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
  • 2Departments of Chemical Engineering, Chemistry, and Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
  • 3Ragon Institute of MGH, MIT, and Harvard, 149 13th Street, Charlestown, Massachusetts 02129, USA

  • *Corresponding author. arupc@mit.edu

Article Text (Subscription Required)

Click to Expand

Supplemental Material (Subscription Required)

Click to Expand

References (Subscription Required)

Click to Expand
Issue

Vol. 108, Iss. 5 — 3 February 2012

Reuse & Permissions
Access Options
Author publication services for translation and copyediting assistance advertisement

Authorization Required


×
×

Images

×

Sign up to receive regular email alerts from Physical Review Letters

Log In

Cancel
×

Search


Article Lookup

Paste a citation or DOI

Enter a citation
×