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

Stochastic Modeling of Autoregulatory Genetic Feedback Loops: A Review and Comparative Study. 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biophysical journal Pubmed

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

Biophysical journal, Volume 118 (7): 9 – May 5, 2020
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Stochastic Modeling of Autoregulatory Genetic Feedback Loops: A Review and Comparative Study.

Biophysical journal, Volume 118 (7): 9 – May 5, 2020

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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DOI
10.1016/j.bpj.2020.02.016
pmid
32155410

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.

Journal

Biophysical journalPubmed

Published: May 5, 2020

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