Show simple item record

dc.contributor.authorSuccurro, Antonellaen_US
dc.contributor.authorSegre, Danielen_US
dc.contributor.authorEbenhoeh, Oliveren_US
dc.date.accessioned2020-01-28T18:25:10Z
dc.date.available2020-01-28T18:25:10Z
dc.date.issued2019-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000460343800018&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationAntonella Succurro, Daniel Segre, Oliver Ebenhoeh. 2019. "Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of Escherichia coli Diauxic Growth." MSYSTEMS, Volume 4, Issue 1, pp. ? - ? (16). https://doi.org/10.1128/mSystems.00230-18
dc.identifier.issn2379-5077
dc.identifier.urihttps://hdl.handle.net/2144/39193
dc.description.abstractMicrobes have adapted to greatly variable environments in order to survive both short-term perturbations and permanent changes. A classical and yet still actively studied example of adaptation to dynamic environments is the diauxic shift of Escherichia coli, in which cells grow on glucose until its exhaustion and then transition to using previously secreted acetate. Here we tested different hypotheses concerning the nature of this transition by using dynamic metabolic modeling. To reach this goal, we developed an open source modeling framework integrating dynamic models (ordinary differential equation systems) with structural models (metabolic networks) which can take into account the behavior of multiple subpopulations and smooth flux transitions between time points. We used this framework to model the diauxic shift, first with a single E. coli model whose metabolic state represents the overall population average and then with a community of two subpopulations, each growing exclusively on one carbon source (glucose or acetate). After introduction of an environment-dependent transition function that determined the balance between subpopulations, our model generated predictions that are in strong agreement with published data. Our results thus support recent experimental evidence that diauxie, rather than a coordinated metabolic shift, would be the emergent pattern of individual cells differentiating for optimal growth on different substrates. This work offers a new perspective on the use of dynamic metabolic modeling to investigate population heterogeneity dynamics. The proposed approach can easily be applied to other biological systems composed of metabolically distinct, interconverting subpopulations and could be extended to include single-cell-level stochasticity.en_US
dc.description.sponsorshipA.S. and O.E. are supported by the Deutsche Forschungsgemeinschaft, Cluster of Excellence on Plant Sciences CEPLAS (EXC 1028). A.S. was supported also by funding from the European Commission Seventh Framework Marie Curie Initial Training Network project AccliPhot (grant agreement PITN-GA-2012-316427). D.S. acknowledges funding from the U.S. Department of Energy (DE-SC0012627), the NIH (5R01DE024468 and R01GM121950), the National Science Foundation (grants 1457695 and NSFOCE-BSF 1635070), MURI grant W911NF-12-1-0390, the Human Frontiers Science Program (RGP0020/2016), and the Boston University Interdisciplinary Biomedical Research Office. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. (EXC 1028 - Deutsche Forschungsgemeinschaft, Cluster of Excellence on Plant Sciences CEPLAS; PITN-GA-2012-316427 - European Commission Seventh Framework Marie Curie Initial Training Network project AccliPhot; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; 1457695 - National Science Foundation; NSFOCE-BSF 1635070 - National Science Foundation; W911NF-12-1-0390 - MURI; RGP0020/2016 - Human Frontiers Science Program; Boston University Interdisciplinary Biomedical Research Office)en_US
dc.format.extent16 p.en_US
dc.languageEnglish
dc.language.isoen_US
dc.publisherAMER SOC MICROBIOLOGYen_US
dc.relation.ispartofMSYSTEMS
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & technologyen_US
dc.subjectLife sciences & biomedicineen_US
dc.subjectMicrobiologyen_US
dc.subjectDiauxic growthen_US
dc.subjectMetabolic network modelingen_US
dc.subjectMicrobial communitiesen_US
dc.subjectPopulation heterogeneityen_US
dc.subjectProtein-turnoveren_US
dc.subjectDeterminesen_US
dc.titleEmergent subpopulation behavior uncovered with a community dynamic metabolic model of Escherichia coli diauxic growthen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1128/mSystems.00230-18
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Biologyen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0003-4859-1914 (Segre, Daniel)
dc.identifier.mycv453822


This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International