Controlling gene expression timing through gene regulatory architecture
Gene networks typically involve the regulatory control of multiple genes with related function. This connectivity enables correlated control of the levels and timing of gene expression. Here we study how gene expression timing in the single-input module motif can be encoded in the regulatory DNA of...
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description | Gene networks typically involve the regulatory control of multiple genes with related function. This connectivity enables correlated control of the levels and timing of gene expression. Here we study how gene expression timing in the single-input module motif can be encoded in the regulatory DNA of a gene. Using stochastic simulations, we examine the role of binding affinity, TF regulatory function and network size in controlling the mean first-passage time to reach a fixed fraction of steady-state expression for both an auto-regulated TF gene and a target gene. We also examine how the variability in first-passage time depends on these factors. We find that both network size and binding affinity can dramatically speed up or slow down the response time of network genes, in some cases predicting more than a 100-fold change compared to that for a constitutive gene. Furthermore, these factors can also significantly impact the fidelity of this response. Importantly, these effects do not occur at "extremes" of network size or binding affinity, but rather in an intermediate window of either quantity. |
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This connectivity enables correlated control of the levels and timing of gene expression. Here we study how gene expression timing in the single-input module motif can be encoded in the regulatory DNA of a gene. Using stochastic simulations, we examine the role of binding affinity, TF regulatory function and network size in controlling the mean first-passage time to reach a fixed fraction of steady-state expression for both an auto-regulated TF gene and a target gene. We also examine how the variability in first-passage time depends on these factors. We find that both network size and binding affinity can dramatically speed up or slow down the response time of network genes, in some cases predicting more than a 100-fold change compared to that for a constitutive gene. Furthermore, these factors can also significantly impact the fidelity of this response. Importantly, these effects do not occur at "extremes" of network size or binding affinity, but rather in an intermediate window of either quantity.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1009745</identifier><identifier>PMID: 35041641</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Affinity ; Binding ; Binding sites ; Biology and Life Sciences ; Cell division ; Computer and Information Sciences ; Computer Simulation ; Deoxyribonucleic acid ; DNA ; E coli ; Gene expression ; Gene Expression Regulation - genetics ; Gene Regulatory Networks - genetics ; Genes ; Genes, Regulator - genetics ; Models, Genetic ; Observations ; Protein Binding ; Proteins ; Response time ; Stochasticity ; Transcription Factors - genetics ; Transcription Factors - metabolism</subject><ispartof>PLoS computational biology, 2022-01, Vol.18 (1), p.e1009745-e1009745</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Ali, Brewster. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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This connectivity enables correlated control of the levels and timing of gene expression. Here we study how gene expression timing in the single-input module motif can be encoded in the regulatory DNA of a gene. Using stochastic simulations, we examine the role of binding affinity, TF regulatory function and network size in controlling the mean first-passage time to reach a fixed fraction of steady-state expression for both an auto-regulated TF gene and a target gene. We also examine how the variability in first-passage time depends on these factors. We find that both network size and binding affinity can dramatically speed up or slow down the response time of network genes, in some cases predicting more than a 100-fold change compared to that for a constitutive gene. Furthermore, these factors can also significantly impact the fidelity of this response. 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genetics</topic><topic>Gene Regulatory Networks - genetics</topic><topic>Genes</topic><topic>Genes, Regulator - genetics</topic><topic>Models, Genetic</topic><topic>Observations</topic><topic>Protein Binding</topic><topic>Proteins</topic><topic>Response time</topic><topic>Stochasticity</topic><topic>Transcription Factors - genetics</topic><topic>Transcription Factors - metabolism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ali, Md Zulfikar</creatorcontrib><creatorcontrib>Brewster, Robert C</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ali, Md Zulfikar</au><au>Brewster, Robert C</au><au>Morozov, Alexandre V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Controlling gene expression timing through gene regulatory architecture</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2022-01-01</date><risdate>2022</risdate><volume>18</volume><issue>1</issue><spage>e1009745</spage><epage>e1009745</epage><pages>e1009745-e1009745</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Gene networks typically involve the regulatory control of multiple genes with related function. This connectivity enables correlated control of the levels and timing of gene expression. Here we study how gene expression timing in the single-input module motif can be encoded in the regulatory DNA of a gene. Using stochastic simulations, we examine the role of binding affinity, TF regulatory function and network size in controlling the mean first-passage time to reach a fixed fraction of steady-state expression for both an auto-regulated TF gene and a target gene. We also examine how the variability in first-passage time depends on these factors. We find that both network size and binding affinity can dramatically speed up or slow down the response time of network genes, in some cases predicting more than a 100-fold change compared to that for a constitutive gene. Furthermore, these factors can also significantly impact the fidelity of this response. Importantly, these effects do not occur at "extremes" of network size or binding affinity, but rather in an intermediate window of either quantity.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35041641</pmid><doi>10.1371/journal.pcbi.1009745</doi><orcidid>https://orcid.org/0000-0002-7656-4086</orcidid><orcidid>https://orcid.org/0000-0002-7054-0059</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Affinity Binding Binding sites Biology and Life Sciences Cell division Computer and Information Sciences Computer Simulation Deoxyribonucleic acid DNA E coli Gene expression Gene Expression Regulation - genetics Gene Regulatory Networks - genetics Genes Genes, Regulator - genetics Models, Genetic Observations Protein Binding Proteins Response time Stochasticity Transcription Factors - genetics Transcription Factors - metabolism |
title | Controlling gene expression timing through gene regulatory architecture |
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