title: Identifying dynamical bottlenecks of stochastic transitions in biochemical networks.
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.
authors.affiliation: Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Govern
authors.forename: Ming
authors.initials: M
authors.lastname: Yang
authors.forename: Arup K
authors.initials: AK
authors.lastname: Chakraborty
mesh_terms: D000465:Algorithms; D000818:Animals; D001669:Biochemical Phenomena / Q000502:physiology*; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D020342:Finite Element Analysis; D019204:GTP-Binding Proteins / Q000737:chemistry / Q000235:genetics; D015870:Gene Expression / Q000502:physiology; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry; D013269:Stochastic Processes; D018631:ras Proteins / Q000737:chemistry / Q000235:genetics
chemical_list: D011506:Proteins; D019204:GTP-Binding Proteins; D018631:ras Proteins