文献类型：期刊文献

英文题名：Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells

作者：Cao, Zhixing[1,2];Grima, Ramon[2]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Edinburgh, Sch Biol Sci, Edinburgh EH9 3BF, Midlothian, Scotland

年份：2020

卷号：117

期号：9

起止页码：4682

外文期刊名：PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000518473500040)】；

基金：Z.C. was supported by the UK Research Councils' Synthetic Biology for Growth program, the Biotechnology and Biological Sciences Research Council (BBSRC), the Engineering and Physical Sciences Research Council, and Medical Research Council Grant BB/M018040/1. R.G. was supported by BBSRC Grant BB/M025551/1. R.G. thanks Sara Buonomo and Peter Swain for useful discussions and insightful feedback.

语种：英文

外文关键词：stochastic gene expression; master equation; perturbation theory

摘要：The stochasticity of gene expression presents significant challenges to the modeling of genetic networks. A two-state model describing promoter switching, transcription, and messenger RNA (mRNA) decay is the standard model of stochastic mRNA dynamics in eukaryotic cells. Here, we extend this model to include mRNA maturation, cell division, gene replication, dosage compensation, and growth-dependent transcription. We derive expressions for the time-dependent distributions of nascent mRNA and mature mRNA numbers, provided two assumptions hold: 1) nascent mRNA dynamics are much faster than those of mature mRNA; and 2) gene-inactivation events occur far more frequently than gene-activation events. We confirm that thousands of eukaryotic genes satisfy these assumptions by using data from yeast, mouse, and human cells. We use the expressions to perform a sensitivity analysis of the coefficient of variation of mRNA fluctuations averaged over the cell cycle, for a large number of genes in mouse embryonic stem cells, identifying degradation and gene-activation rates as the most sensitive parameters. Furthermore, it is shown that, despite the model's complexity, the time-dependent distributions predicted by our model are generally well approximated by the negative binomial distribution. Finally, we extend our model to include translation, protein decay, and auto-regulatory feedback, and derive expressions for the approximate time-dependent protein-number distributions, assuming slow protein decay. Our expressions enable us to study how complex biological processes contribute to the fluctuations of gene products in eukaryotic cells, as well as allowing a detailed quantitative comparison with experimental data via maximum-likelihood methods.