Holehouse James, Cao Zhixing, Grima Ramon
School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.
School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom; The Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China.
Biophys J. 2020 Apr 7;118(7):1517-1525. doi: 10.1016/j.bpj.2020.02.016. Epub 2020 Feb 25.
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 steady-state 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.
自调节反馈回路是最常见的网络基序之一。人们构建了各种各样的随机模型，以了解这些回路中蛋白质数量的波动如何受到主要生化步骤动力学参数的影响。这些模型的不同之处在于：1）明确建模的亚细胞过程；2）采用的建模方法（离散、连续或混合）；3）能否针对蛋白质数量的稳态分布进行解析求解。我们讨论了文献中主要模型的假设和特性，总结了我们目前对它们之间关系的理解，并强调了通过建模获得的一些见解。
