Linear mapping approximation of gene regulatory networks with stochastic dynamics.

ABSTRACT: The presence of protein-DNA binding reactions often leads to analytically intractable models of stochastic gene expression. Here we present the linear-mapping approximation that maps systems with protein-promoter interactions onto approximately equivalent systems with no binding reactions. This is achieved by the marriage of conditional mean-field approximation and the Magnus expansion, leading to analytic or semi-analytic expressions for the approximate time-dependent and steady-state protein number distributions. Stochastic simulations verify the method's accuracy in capturing the changes in the protein number distributions with time for a wide variety of networks displaying auto- and mutual-regulation of gene expression and independently of the ratios of the timescales governing the dyn

SUBMITTER: Cao Z 

PROVIDER: S-EPMC6098115 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature