Emergent sub-population behavior uncovered with a community dynamic metabolic model of Escherichia coli diauxic growth

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Abstract

Abstract Microbes have adapted to greatly variable environments in order to survive both short-term perturbations and permanent changes. A classical, 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. Towards 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 sub-populations, and smooth ux transitions between different 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 sub-populations each growing exclusively on one carbon source (glucose or acetate). After introducing an environment-dependent transition function that determines the balance between different sub-populations, our model generates predictions that are in strong agreement with published data. We thus support recent experimental evidence that, rather than a coordinated metabolic shift, diauxie would be the emergent pattern of individual cells differentiating for optimal growth on different sub-strates. 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 sub-populations, and could be extended to include single-cell level stochasticity. Importance Escherichia coli diauxie is a fundamental example of metabolic adaptation 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 sub-population dynamics in E. coli monocultures. Without fine tuning the parameters of the E. coli core metabolic model, we achieve 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 we provide can be applied to model general sub-population systems in more complex environments, and can be extended to include single-cell level stochasticity.

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