Work

Identifying Dynamical Bottlenecks of Stochastic Transitions in Biochemical Networks

Year: 2012

Type: article

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.

Source: Physical Review Letters

DOI: 10.1103/physrevlett.108.058102

Authors: Christopher C. Govern, Ming Yang, Arup K. Chakraborty

Institutions: Massachusetts Institute of Technology, Ragon Institute of MGH, MIT and Harvard

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Cancer Institute, National Institute of Allergy and Infectious Diseases

Award: Defining the Unique Properties of the Distinct Signaling Machinery Used by the TCR