文献类型：期刊文献

英文题名：Stochastic Modeling of Autoregulatory Genetic Feedback Loops: A Review and Comparative Study

作者：Holehouse, James[1];Cao, Zhixing[1,2];Grima, Ramon[1]

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

年份：2020

卷号：118

期号：7

起止页码：1517

外文期刊名：BIOPHYSICAL JOURNAL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000524456100003)】；

基金：This work was supported by a Biotechnology and Biological Sciences Research Council (BBSRC) EASTBIO PhD studentship, BBSRC grant BBIM025551/1, and the UK Research Councils' Synthetic Biology for Growth program of the BBSRC, Engineering and Physical Sciences Research Council, and Medical Research Council (BB/M018040/1).

语种：英文

摘要：Autoregulatory feedback loops are one of the most common network motifs. A wide variety of stochastic models have been constructed to understand how the fluctuations in protein numbers in these loops are influenced by the kinetic parameters of the main biochemical steps. These models differ according to 1)which subcellular processes are explicitly modeled, 2) the modeling methodology employed (discrete, continuous, or hybrid), and 3) whether they can be analytically solved for the steadystate distribution of protein numbers. We discuss the assumptions and properties of the main models in the literature, summarize our current understanding of the relationship between them, and highlight some of the insights gained through modeling.