Gene regulatory networks driven by intrinsic noise with two-time scales： a stochastic averaging approach
Gene regulatory networks driven by intrinsic noise with two-time scales： a stochastic averaging approach作者机构：School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074 China Department of Mathematics Wayne State University Detroit MI 48202 USA School of Mathematical Sciences Monash University Melbourne Vic 3800 Australia
出 版 物：《Frontiers of Mathematics in China》 (中国高等学校学术文摘·数学（英文）)
年 卷 期：2014年第9卷第4期
页 面：947-963页
核心收录：
学科分类：0710[理学-生物学] 07[理学] 08[工学] 071009[理学-细胞生物学] 09[农学] 0901[农学-作物学] 0701[理学-数学] 080101[工学-一般力学与力学基础] 090102[农学-作物遗传育种] 0801[工学-力学（可授工学、理学学位）]
基 金：supported in part by the Program for New Century Excellent Talents in University the Fundamental Research Funds for the Central Universities supported in part by the Army Research Office under supported by Australian Research Council Discovery Project
主 题：Two-time scales intrinsic noise intracellular reaction chemicalLangevin equation (CLE) stationary distribution
摘 要：This work focuses on gene regulatory networks driven by intrinsic noise with two-time scales. It uses a stochastic averaging approach for these systems to reduce complexity. Comparing with the traditional quasi-steady- state hypothesis （QSSH）, our approach uses stochastic averaging principle to treat the intrinsic noise coming from both the fast-changing variables and the slow-changing variables, which yields a more precise description of the underlying systems. To provide further insight, this paper also investigates a prototypical two-component activator-repressor genetic circuit model as an example. If all the protein productions were linear, these two methods would yield the same reduction result. However, if one of the protein productions is nonlinear, the stochastic averaging principle leads to a different reduction result from that of the traditional QSSH.
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