Agent-based modeling of oxygen-responsive transcription factors in Escherichia coli
In the presence of oxygen (O2) the model bacterium Escherichia coli is able to conserve energy by aerobic respiration. Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high...
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description | In the presence of oxygen (O2) the model bacterium Escherichia coli is able to conserve energy by aerobic respiration. Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high affinity for O2 but does not pump protons. When E. coli encounters environments with different O2 availabilities, the expression of the genes encoding the alternative terminal oxidases, the cydAB and cyoABCDE operons, are regulated by two O2-responsive transcription factors, ArcA (an indirect O2 sensor) and FNR (a direct O2 sensor). It has been suggested that O2-consumption by the terminal oxidases located at the cytoplasmic membrane significantly affects the activities of ArcA and FNR in the bacterial nucleoid. In this study, an agent-based modeling approach has been taken to spatially simulate the uptake and consumption of O2 by E. coli and the consequent modulation of ArcA and FNR activities based on experimental data obtained from highly controlled chemostat cultures. The molecules of O2, transcription factors and terminal oxidases are treated as individual agents and their behaviors and interactions are imitated in a simulated 3-D E. coli cell. The model implies that there are two barriers that dampen the response of FNR to O2, i.e. consumption of O2 at the membrane by the terminal oxidases and reaction of O2 with cytoplasmic FNR. Analysis of FNR variants suggested that the monomer-dimer transition is the key step in FNR-mediated repression of gene expression. |
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Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high affinity for O2 but does not pump protons. When E. coli encounters environments with different O2 availabilities, the expression of the genes encoding the alternative terminal oxidases, the cydAB and cyoABCDE operons, are regulated by two O2-responsive transcription factors, ArcA (an indirect O2 sensor) and FNR (a direct O2 sensor). It has been suggested that O2-consumption by the terminal oxidases located at the cytoplasmic membrane significantly affects the activities of ArcA and FNR in the bacterial nucleoid. In this study, an agent-based modeling approach has been taken to spatially simulate the uptake and consumption of O2 by E. coli and the consequent modulation of ArcA and FNR activities based on experimental data obtained from highly controlled chemostat cultures. The molecules of O2, transcription factors and terminal oxidases are treated as individual agents and their behaviors and interactions are imitated in a simulated 3-D E. coli cell. The model implies that there are two barriers that dampen the response of FNR to O2, i.e. consumption of O2 at the membrane by the terminal oxidases and reaction of O2 with cytoplasmic FNR. Analysis of FNR variants suggested that the monomer-dimer transition is the key step in FNR-mediated repression of gene expression.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1003595</identifier><identifier>PMID: 24763195</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Bacteriology ; Biology and Life Sciences ; Computer and Information Sciences ; Deoxyribonucleic acid ; DNA ; E coli ; Escherichia coli - genetics ; Escherichia coli - metabolism ; Gene expression ; Genes, Bacterial ; Operon ; Oxygen ; Oxygen - metabolism ; Plasmids ; Transcription factors ; Transcription Factors - metabolism</subject><ispartof>PLoS computational biology, 2014-04, Vol.10 (4), p.e1003595-e1003595</ispartof><rights>2014 Bai et al 2014 Bai et al</rights><rights>2014 Bai et al. 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 credited: Bai H, Rolfe MD, Jia W, Coakley S, Poole RK, et al. (2014) Agent-Based Modeling of Oxygen-Responsive Transcription Factors in Escherichia coli. 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Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high affinity for O2 but does not pump protons. When E. coli encounters environments with different O2 availabilities, the expression of the genes encoding the alternative terminal oxidases, the cydAB and cyoABCDE operons, are regulated by two O2-responsive transcription factors, ArcA (an indirect O2 sensor) and FNR (a direct O2 sensor). It has been suggested that O2-consumption by the terminal oxidases located at the cytoplasmic membrane significantly affects the activities of ArcA and FNR in the bacterial nucleoid. In this study, an agent-based modeling approach has been taken to spatially simulate the uptake and consumption of O2 by E. coli and the consequent modulation of ArcA and FNR activities based on experimental data obtained from highly controlled chemostat cultures. The molecules of O2, transcription factors and terminal oxidases are treated as individual agents and their behaviors and interactions are imitated in a simulated 3-D E. coli cell. The model implies that there are two barriers that dampen the response of FNR to O2, i.e. consumption of O2 at the membrane by the terminal oxidases and reaction of O2 with cytoplasmic FNR. Analysis of FNR variants suggested that the monomer-dimer transition is the key step in FNR-mediated repression of gene expression.</description><subject>Bacteriology</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>E coli</subject><subject>Escherichia coli - genetics</subject><subject>Escherichia coli - metabolism</subject><subject>Gene expression</subject><subject>Genes, Bacterial</subject><subject>Operon</subject><subject>Oxygen</subject><subject>Oxygen - metabolism</subject><subject>Plasmids</subject><subject>Transcription factors</subject><subject>Transcription Factors - metabolism</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNpVUU1P3DAQtapWBbb9B1WbYy_ZevyR2JdKCFFAQuqhcLacsbPrVTZO7Swq_74JGxCcbM978-Z5HiFfgK6B1_BjFw-pt916wCasgVIutXxHTkFKXtZcqvev7ifkLOfdzFG6-khOmKgrDlqekj_nG9-PZWOzd8U-Ot-FflPEtoj_HiekTD4Psc_hwRdjsn3GFIYxxL5oLY4x5SL0xWXGrU8Bt8EWGLvwiXxobZf95-Vckftfl3cX1-Xt76ubi_PbEoVWYwnUC10hVU6zRkiFDCrHvWNWoagVWu0cVJWyKBkIgJp6JbEV1DZI65bzFfl21B26mM2yj2xAMsm0Fgomxs2R4aLdmSGFvU2PJtpgngoxbYxNY8DOG9rwFgVlAoQVbQPKIWPaNRymRzOhK_JzmXZo9t7htLZkuzeib5E-bM0mPhiutVJ6NvN9EUjx78Hn0exDRt91tvfxMPsGzarJgp6o4kjFFHNOvn0ZA9TM6T__1szpmyX9qe3ra4svTc9x8_870K99</recordid><startdate>20140401</startdate><enddate>20140401</enddate><creator>Bai, Hao</creator><creator>Rolfe, Matthew D</creator><creator>Jia, Wenjing</creator><creator>Coakley, Simon</creator><creator>Poole, Robert K</creator><creator>Green, Jeffrey</creator><creator>Holcombe, Mike</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20140401</creationdate><title>Agent-based modeling of oxygen-responsive transcription factors in Escherichia coli</title><author>Bai, Hao ; Rolfe, Matthew D ; Jia, Wenjing ; Coakley, Simon ; Poole, Robert K ; Green, Jeffrey ; Holcombe, Mike</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c498t-10e496c08d92b458c216d3ed2a8c478ca9dd1668ac52141170e85cf40abc07f33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Bacteriology</topic><topic>Biology and Life Sciences</topic><topic>Computer and Information Sciences</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>E coli</topic><topic>Escherichia coli - genetics</topic><topic>Escherichia coli - metabolism</topic><topic>Gene expression</topic><topic>Genes, Bacterial</topic><topic>Operon</topic><topic>Oxygen</topic><topic>Oxygen - metabolism</topic><topic>Plasmids</topic><topic>Transcription factors</topic><topic>Transcription Factors - metabolism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bai, Hao</creatorcontrib><creatorcontrib>Rolfe, Matthew D</creatorcontrib><creatorcontrib>Jia, Wenjing</creatorcontrib><creatorcontrib>Coakley, Simon</creatorcontrib><creatorcontrib>Poole, Robert K</creatorcontrib><creatorcontrib>Green, Jeffrey</creatorcontrib><creatorcontrib>Holcombe, Mike</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</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>Bai, Hao</au><au>Rolfe, Matthew D</au><au>Jia, Wenjing</au><au>Coakley, Simon</au><au>Poole, Robert K</au><au>Green, Jeffrey</au><au>Holcombe, Mike</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Agent-based modeling of oxygen-responsive transcription factors in Escherichia coli</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2014-04-01</date><risdate>2014</risdate><volume>10</volume><issue>4</issue><spage>e1003595</spage><epage>e1003595</epage><pages>e1003595-e1003595</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>In the presence of oxygen (O2) the model bacterium Escherichia coli is able to conserve energy by aerobic respiration. Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high affinity for O2 but does not pump protons. When E. coli encounters environments with different O2 availabilities, the expression of the genes encoding the alternative terminal oxidases, the cydAB and cyoABCDE operons, are regulated by two O2-responsive transcription factors, ArcA (an indirect O2 sensor) and FNR (a direct O2 sensor). It has been suggested that O2-consumption by the terminal oxidases located at the cytoplasmic membrane significantly affects the activities of ArcA and FNR in the bacterial nucleoid. In this study, an agent-based modeling approach has been taken to spatially simulate the uptake and consumption of O2 by E. coli and the consequent modulation of ArcA and FNR activities based on experimental data obtained from highly controlled chemostat cultures. The molecules of O2, transcription factors and terminal oxidases are treated as individual agents and their behaviors and interactions are imitated in a simulated 3-D E. coli cell. The model implies that there are two barriers that dampen the response of FNR to O2, i.e. consumption of O2 at the membrane by the terminal oxidases and reaction of O2 with cytoplasmic FNR. Analysis of FNR variants suggested that the monomer-dimer transition is the key step in FNR-mediated repression of gene expression.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24763195</pmid><doi>10.1371/journal.pcbi.1003595</doi><oa>free_for_read</oa></addata></record> |
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subjects | Bacteriology Biology and Life Sciences Computer and Information Sciences Deoxyribonucleic acid DNA E coli Escherichia coli - genetics Escherichia coli - metabolism Gene expression Genes, Bacterial Operon Oxygen Oxygen - metabolism Plasmids Transcription factors Transcription Factors - metabolism |
title | Agent-based modeling of oxygen-responsive transcription factors in Escherichia coli |
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