Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise

Genetic switching driven by noise is a fundamental cellular process in genetic regulatory networks. Quantitatively characterizing this switching and its fluctuation properties is a key problem in computational biology. With an autoregulatory dimer model as a specific example, we design a general met...

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Main Authors:	Lv, Cheng, Li, Xiaoguang, Li, Fangting, Li, Tiejun
Format:	Online
Language:	English
Published:	Public Library of Science 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3923795/

id	pubmed-3923795
recordtype	oai_dc
spelling	pubmed-39237952014-02-18 Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise Lv, Cheng Li, Xiaoguang Li, Fangting Li, Tiejun Research Article Genetic switching driven by noise is a fundamental cellular process in genetic regulatory networks. Quantitatively characterizing this switching and its fluctuation properties is a key problem in computational biology. With an autoregulatory dimer model as a specific example, we design a general methodology to quantitatively understand the metastability of gene regulatory system perturbed by intrinsic noise. Based on the large deviation theory, we develop new analytical techniques to describe and calculate the optimal transition paths between the on and off states. We also construct the global quasi-potential energy landscape for the dimer model. From the obtained quasi-potential, we can extract quantitative results such as the stationary distributions of mRNA, protein and dimer, the noise strength of the expression state, and the mean switching time starting from either stable state. In the final stage, we apply this procedure to a transcriptional cascades model. Our results suggest that the quasi-potential energy landscape and the proposed methodology are general to understand the metastability in other biological systems with intrinsic noise. Public Library of Science 2014-02-13 /pmc/articles/PMC3923795/ /pubmed/24551081 http://dx.doi.org/10.1371/journal.pone.0088167 Text en © 2014 Lv et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Lv, Cheng Li, Xiaoguang Li, Fangting Li, Tiejun
spellingShingle	Lv, Cheng Li, Xiaoguang Li, Fangting Li, Tiejun Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise
author_facet	Lv, Cheng Li, Xiaoguang Li, Fangting Li, Tiejun
author_sort	Lv, Cheng
title	Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise
title_short	Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise
title_full	Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise
title_fullStr	Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise
title_full_unstemmed	Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise
title_sort	constructing the energy landscape for genetic switching system driven by intrinsic noise
description	Genetic switching driven by noise is a fundamental cellular process in genetic regulatory networks. Quantitatively characterizing this switching and its fluctuation properties is a key problem in computational biology. With an autoregulatory dimer model as a specific example, we design a general methodology to quantitatively understand the metastability of gene regulatory system perturbed by intrinsic noise. Based on the large deviation theory, we develop new analytical techniques to describe and calculate the optimal transition paths between the on and off states. We also construct the global quasi-potential energy landscape for the dimer model. From the obtained quasi-potential, we can extract quantitative results such as the stationary distributions of mRNA, protein and dimer, the noise strength of the expression state, and the mean switching time starting from either stable state. In the final stage, we apply this procedure to a transcriptional cascades model. Our results suggest that the quasi-potential energy landscape and the proposed methodology are general to understand the metastability in other biological systems with intrinsic noise.
publisher	Public Library of Science
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3923795/
_version_	1612058271800098816

Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise

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