Title: Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of Escherichia coli Diauxic Growth

Abstract

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 ofmore » 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.« less

Authors:

Succurro, Antonella ;

• Search DOE PAGES for author "Succurro, Antonella"
• Search DOE PAGES for ORCID "0000-0003-0227-2803"

Segrè, Daniel ;

• Search DOE PAGES for author "Segrè, Daniel"
• Search DOE PAGES for ORCID "0000-0003-4859-1914"

Ebenhöh, Oliver ;

• Search DOE PAGES for author "Ebenhöh, Oliver"
• Search DOE PAGES for ORCID "0000-0002-7229-7398"

Glaven, ed., Sarah

Publication Date:

Tue Jan 15 00:00:00 EST 2019

Research Org.:

Boston Univ., MA (United States)

Sponsoring Org.:

USDOE Office of Science (SC); European Research Commission (ERC); National Institutes of Health (NIH); National Science Foundation (NSF); MURI

OSTI Identifier:

1490767

Alternate Identifier(s):

OSTI ID: 1611908

Grant/Contract Number:  

SC0012627; PITN-GA-2012-316427; 5R01DE024458; R01GM121950; 1457695; NSFOCE-BSF 1635070; W911NF-12-1-0390

Resource Type:

Published Article

Journal Name:

mSystems

Additional Journal Information:

Journal Name: mSystems Journal Volume: 4 Journal Issue: 1; Journal ID: ISSN 2379-5077

Publisher:

American Society for Microbiology

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; microbiology; diauxic growth; metabolic network modeling; microbial communities; population heterogeneity