Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth
Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then und...
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Veröffentlicht in: | Bulletin of mathematical biology 2023-09, Vol.85 (9), p.84-84, Article 84 |
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description | Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then undergo a lag phase before resuming growth on the second. Biomass increase is characterised by a diauxic growth curve: exponential growth followed by a period of no growth before a second exponential growth. Recent literature lacks a complete dynamic description, artificially modelling lag phase and employing non-physical representations of precursor pools. Here, we formulate a rational mechanistic model based on flux-regulation/proteome partitioning with a finite precursor pool that reveals core mechanisms in a compact form. Unlike earlier systems, the characteristic dynamics emerge as part of the solution, including the lag phase. Focussing on growth of
Escherichia coli
on a glucose–lactose mixture we show results accurately reproduce experiments. We show that for a single strain of
E. coli
, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains. |
doi_str_mv | 10.1007/s11538-023-01189-6 |
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Escherichia coli
on a glucose–lactose mixture we show results accurately reproduce experiments. We show that for a single strain of
E. coli
, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains.</description><identifier>ISSN: 0092-8240</identifier><identifier>EISSN: 1522-9602</identifier><identifier>DOI: 10.1007/s11538-023-01189-6</identifier><identifier>PMID: 37580520</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Biomass ; Cell Biology ; E coli ; Lactose ; Lag phase ; Life Sciences ; Mathematical and Computational Biology ; Mathematics ; Mathematics and Statistics ; Original ; Original Article ; Precursors ; Proteomes ; Strains (organisms) ; Sugar</subject><ispartof>Bulletin of mathematical biology, 2023-09, Vol.85 (9), p.84-84, Article 84</ispartof><rights>The Author(s) 2023</rights><rights>2023. The Author(s).</rights><rights>The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c426t-8595654a4ca6d39f769ab0429302796ed7f65e1638d626d78e0715ef6485206d3</cites><orcidid>0000-0002-1424-495X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11538-023-01189-6$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11538-023-01189-6$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37580520$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bate, Fiona</creatorcontrib><creatorcontrib>Amekan, Yumechris</creatorcontrib><creatorcontrib>Pushkin, Dmitri O.</creatorcontrib><creatorcontrib>Chong, James P. J.</creatorcontrib><creatorcontrib>Bees, Martin</creatorcontrib><title>Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth</title><title>Bulletin of mathematical biology</title><addtitle>Bull Math Biol</addtitle><addtitle>Bull Math Biol</addtitle><description>Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then undergo a lag phase before resuming growth on the second. Biomass increase is characterised by a diauxic growth curve: exponential growth followed by a period of no growth before a second exponential growth. Recent literature lacks a complete dynamic description, artificially modelling lag phase and employing non-physical representations of precursor pools. Here, we formulate a rational mechanistic model based on flux-regulation/proteome partitioning with a finite precursor pool that reveals core mechanisms in a compact form. Unlike earlier systems, the characteristic dynamics emerge as part of the solution, including the lag phase. Focussing on growth of
Escherichia coli
on a glucose–lactose mixture we show results accurately reproduce experiments. We show that for a single strain of
E. coli
, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains.</description><subject>Biomass</subject><subject>Cell Biology</subject><subject>E coli</subject><subject>Lactose</subject><subject>Lag phase</subject><subject>Life Sciences</subject><subject>Mathematical and Computational Biology</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Original</subject><subject>Original Article</subject><subject>Precursors</subject><subject>Proteomes</subject><subject>Strains (organisms)</subject><subject>Sugar</subject><issn>0092-8240</issn><issn>1522-9602</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp9kc1P3DAQxa0K1F22_Qc4oEhcuLiM7dixT3wJaKWtWiF6trzJJBuUjcFOaPnva7p0-ThwmsP83ps3eoTsMvjCAIrDyJgUmgIXFBjThqoPZMok59Qo4FtkCmA41TyHCdmJ8QaSyAjzkUxEITVIDlNyfL7C0GA_ZHPXZD-XLmLW9tlFN_6hV9iMnRta32fffYVdzHydnbpywNC6LrsM_vew_ES2a9dF_Pw0Z-TXxfn12Vc6_3H57exkTsucq4FqaaSSuctLpyph6kIZt4CcGwG8MAqrolYSmRK6UlxVhUYomMRa5TrlTJIZOVr73o6LFVZlihxcZ29Du3LhwXrX2tebvl3axt9blq5IySA5HDw5BH83Yhzsqo0ldp3r0Y_Rci0ZywUUOqH7b9AbP4Y-_fdIgdI50yJRfE2VwccYsN6kYWAfG7LrhmxqyP5ryKok2nv5x0byv5IEiDUQ06pvMDzffsf2LytYmaA</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Bate, Fiona</creator><creator>Amekan, Yumechris</creator><creator>Pushkin, Dmitri O.</creator><creator>Chong, James P. 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J. ; Bees, Martin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-8595654a4ca6d39f769ab0429302796ed7f65e1638d626d78e0715ef6485206d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Biomass</topic><topic>Cell Biology</topic><topic>E coli</topic><topic>Lactose</topic><topic>Lag phase</topic><topic>Life Sciences</topic><topic>Mathematical and Computational Biology</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Original</topic><topic>Original Article</topic><topic>Precursors</topic><topic>Proteomes</topic><topic>Strains (organisms)</topic><topic>Sugar</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bate, Fiona</creatorcontrib><creatorcontrib>Amekan, Yumechris</creatorcontrib><creatorcontrib>Pushkin, Dmitri O.</creatorcontrib><creatorcontrib>Chong, James P. J.</creatorcontrib><creatorcontrib>Bees, Martin</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bulletin of mathematical biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bate, Fiona</au><au>Amekan, Yumechris</au><au>Pushkin, Dmitri O.</au><au>Chong, James P. J.</au><au>Bees, Martin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth</atitle><jtitle>Bulletin of mathematical biology</jtitle><stitle>Bull Math Biol</stitle><addtitle>Bull Math Biol</addtitle><date>2023-09-01</date><risdate>2023</risdate><volume>85</volume><issue>9</issue><spage>84</spage><epage>84</epage><pages>84-84</pages><artnum>84</artnum><issn>0092-8240</issn><eissn>1522-9602</eissn><abstract>Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then undergo a lag phase before resuming growth on the second. Biomass increase is characterised by a diauxic growth curve: exponential growth followed by a period of no growth before a second exponential growth. Recent literature lacks a complete dynamic description, artificially modelling lag phase and employing non-physical representations of precursor pools. Here, we formulate a rational mechanistic model based on flux-regulation/proteome partitioning with a finite precursor pool that reveals core mechanisms in a compact form. Unlike earlier systems, the characteristic dynamics emerge as part of the solution, including the lag phase. Focussing on growth of
Escherichia coli
on a glucose–lactose mixture we show results accurately reproduce experiments. We show that for a single strain of
E. coli
, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>37580520</pmid><doi>10.1007/s11538-023-01189-6</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-1424-495X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Biomass Cell Biology E coli Lactose Lag phase Life Sciences Mathematical and Computational Biology Mathematics Mathematics and Statistics Original Original Article Precursors Proteomes Strains (organisms) Sugar |
title | Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth |
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