Identifying Dynamical Bottlenecks of Stochastic Transitions in Biochemical Networks

被引:3
|
作者
Govern, Christopher C. [1 ]
Yang, Ming [1 ]
Chakraborty, Arup K. [1 ,2 ,3 ,4 ]
机构
[1] MIT, Dept Chem Engn, Cambridge, MA 02139 USA
[2] MIT, Dept Chem, Cambridge, MA 02139 USA
[3] MIT, Dept Biol Engn, Cambridge, MA 02139 USA
[4] MIT, MGH, Ragon Inst, Charlestown, MA 02129 USA
关键词
GENE-EXPRESSION; FLUCTUATIONS; SYSTEMS; PATHS; RAS;
D O I
10.1103/PhysRevLett.108.058102
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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.
引用
收藏
页数:5
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