Identifying Dynamical Bottlenecks of Stochastic Transitions in Biochemical Networks
Author(s)Yang, Ming; Chakraborty, Arup K.; Govern, Christopher C.
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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.
DepartmentMassachusetts 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
Physical Review Letters
American Physical Society
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
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