Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of <italic toggle="yes">Escherichia coli</italic> Diauxic Growth

ABSTRACT Microbes 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 u...

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Autores principales:	Antonella Succurro, Daniel Segrè, Oliver Ebenhöh
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2019
Materias:	diauxic growth metabolic network modeling microbial communities population heterogeneity Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/c8b740f819354fc4afc232633d3cfdb2
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spelling	oai:doaj.org-article:c8b740f819354fc4afc232633d3cfdb22021-12-02T18:39:16ZEmergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of <italic toggle="yes">Escherichia coli</italic> Diauxic Growth10.1128/mSystems.00230-182379-5077https://doaj.org/article/c8b740f819354fc4afc232633d3cfdb22019-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00230-18https://doaj.org/toc/2379-5077ABSTRACT Microbes 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. IMPORTANCE Escherichia coli diauxie is a fundamental example of metabolic adaptation, a phenomenon that is not yet completely understood. Further insight into this process can be achieved by integrating experimental and computational modeling methods. We present a dynamic metabolic modeling approach that captures diauxie as an emergent property of subpopulation dynamics in E. coli monocultures. Without fine-tuning the parameters of the E. coli core metabolic model, we achieved good agreement with published data. Our results suggest that single-organism metabolic models can only approximate the average metabolic state of a population, therefore offering a new perspective on the use of such modeling approaches. The open source modeling framework that we provide can be applied to model general subpopulation systems in more-complex environments and can be extended to include single-cell-level stochasticity.Antonella SuccurroDaniel SegrèOliver EbenhöhAmerican Society for Microbiologyarticlediauxic growthmetabolic network modelingmicrobial communitiespopulation heterogeneityMicrobiologyQR1-502ENmSystems, Vol 4, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	diauxic growth metabolic network modeling microbial communities population heterogeneity Microbiology QR1-502
spellingShingle	diauxic growth metabolic network modeling microbial communities population heterogeneity Microbiology QR1-502 Antonella Succurro Daniel Segrè Oliver Ebenhöh Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of <italic toggle="yes">Escherichia coli</italic> Diauxic Growth
description	ABSTRACT Microbes 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. IMPORTANCE Escherichia coli diauxie is a fundamental example of metabolic adaptation, a phenomenon that is not yet completely understood. Further insight into this process can be achieved by integrating experimental and computational modeling methods. We present a dynamic metabolic modeling approach that captures diauxie as an emergent property of subpopulation dynamics in E. coli monocultures. Without fine-tuning the parameters of the E. coli core metabolic model, we achieved good agreement with published data. Our results suggest that single-organism metabolic models can only approximate the average metabolic state of a population, therefore offering a new perspective on the use of such modeling approaches. The open source modeling framework that we provide can be applied to model general subpopulation systems in more-complex environments and can be extended to include single-cell-level stochasticity.
format	article
author	Antonella Succurro Daniel Segrè Oliver Ebenhöh
author_facet	Antonella Succurro Daniel Segrè Oliver Ebenhöh
author_sort	Antonella Succurro
title	Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of <italic toggle="yes">Escherichia coli</italic> Diauxic Growth
title_short	Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of <italic toggle="yes">Escherichia coli</italic> Diauxic Growth
title_full	Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of <italic toggle="yes">Escherichia coli</italic> Diauxic Growth
title_fullStr	Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of <italic toggle="yes">Escherichia coli</italic> Diauxic Growth
title_full_unstemmed	Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of <italic toggle="yes">Escherichia coli</italic> Diauxic Growth
title_sort	emergent subpopulation behavior uncovered with a community dynamic metabolic model of <italic toggle="yes">escherichia coli</italic> diauxic growth
publisher	American Society for Microbiology
publishDate	2019
url	https://doaj.org/article/c8b740f819354fc4afc232633d3cfdb2
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Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of <italic toggle="yes">Escherichia coli</italic> Diauxic Growth

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