Stochastic Modeling of Autoregulatory Genetic Feedback Loops: A Review and Comparative Study.

Biophysical Journal
James HolehouseRamon Grima

Abstract

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.

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Citations

May 10, 2020·The Journal of Chemical Physics·Chen Jia, Ramon Grima
Jan 9, 2021·Frontiers in Cell and Developmental Biology·Mona K TonnDiego A Oyarzún
Jun 3, 2020·Current Opinion in Biotechnology·Zhixing CaoLixin Zhang
Jul 3, 2021·Mathematical Biosciences and Engineering : MBE·Meiling ChenJiajun Zhang
Aug 20, 2020·Biophysical Journal·Zhixing CaoRamon Grima

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