RT Journal Article
SR Electronic
T1 Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.07.19.549675
DO 10.1101/2023.07.19.549675
A1 Wang, Xinyu
A1 Li, Youming
A1 Jia, Chen
YR 2023
UL http://biorxiv.org/content/early/2023/07/19/2023.07.19.549675.1.abstract
AB Stochastic gene expression dynamics can be modeled either discretely or continuously. Previous studies have shown that the mRNA or protein number distributions of some simple discrete and continuous gene expression models are related by Gardiner’s Poisson representation. Here we systematically investigate the Poisson representation in complex stochastic gene regulatory networks. We show that when the gene of interest is unregulated, the discrete and continuous descriptions of stochastic gene expression are always related by the Poisson representation, no matter how complex the model is. In addition, using a simple counterexample, we find that the Poisson representation in general fails to link the two descriptions when the gene is regulated. However, for a general stochastic gene regulatory network, we demonstrate that the discrete and continuous models are approximately related by the Poisson representation in the limit of large protein numbers. These theoretical results are further applied to analytically solve many complex gene expression models whose exact distributions are previously unknown.Competing Interest StatementThe authors have declared no competing interest.