Modeling microbial metabolic trade-offs in a chemostat

Li, Zhiyuan; Liu, Bo; Li, Sophia Hsin-Jung; King, C. H.; Gitai, Zemer; Wingreen, Ned S.

doi:10.1371/journal.pcbi.1008156

Cited by 34 publications

(33 citation statements)

References 60 publications

(86 reference statements)

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“…For CA, a CS emerges without interspecies communication, simply because, at the species level, the growth rates of species 1 and 2 are limited by the uptake rates of A e and B e , respectively. This is a known result from chemostat modelling [18]. Thus, at their steady state concentrations, the species reach a self stabilizing equilibrium, where neither species can grow faster than D = 1.…”

Section: Coexistence Microbial Consortiummentioning

confidence: 65%

“…Different combinations of microbial and substrate concentrations may give rise to multiple valid steady states. Models that take the extracellular environment into account are frequent in the chemostat literature [18,19]. However, the illustrative small scale models conventionally used in chemostat modelling do not possess intracellular metabolic networks with degrees of freedom in the fluxes, and are thus not concerned with decision making in the same way as FBA-models that use internal degrees of freedom to optimize some objective.…”

Section: Chemostat Vs Batch Environmentmentioning

confidence: 99%

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Microbial Community Decision Making Models in Batch and Chemostat Cultures

Theorell

Stelling

2021

Computational Methods in Systems Biology

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Microbial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically. Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Crucially, our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor).

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Section: Coexistence Microbial Consortiummentioning

confidence: 65%

Section: Chemostat Vs Batch Environmentmentioning

confidence: 99%

Microbial Community Decision Making Models in Batch and Chemostat Cultures

Theorell

Stelling

2021

Computational Methods in Systems Biology

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“…predation) [28], or rapid evolution [29]. Recent work has suggested that both thermodynamic inhibition [6] and metabolic trade-offs [7] can allow more survivors than substrates, though it has since been suggested that metabolic trade-offs only support increased diversity for a very narrow and specific parameter range [30]. The proteomic trade-off that our model includes represents a more detailed version of the metabolic trade-off previous work has considered, where instead of a fixed "enzyme budget" the fraction of the proteome that is available to split between reactions inversely proportional to the size of the ribosome fraction.…”

Section: Discussionmentioning

confidence: 99%

Thermodynamic constraints on the assembly and diversity of microbial ecosystems are different near to and far from equilibrium

Cook

Pawar

Endres

2021

Preprint

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Most biological processes are driven by non-equilibrium thermodynamics, but despite significant progress in theoretical ecology the constraints this places on ecosystem dynamics has been barely considered. Microbial ecosystems represent a natural place to begin this consideration, as many of the ways they interact, such as metabolite production and cross-feeding, can be described in thermodynamic terms. Previous work considered the impact of thermodynamics such as the rate-yield trade-off on individual species' competitive ability, but restrained from analyzing complex dynamical systems. To address this gap we developed a thermodynamic microbial consumer-resource model with fully reversible reaction kinetics, which allows direct consideration of free-energy dissipation. Using this model, we show that ecosystem diversity increases with supplied free energy, because greater availability of free energy allows for faster ecosystem development. Thus, when species from the initial community begin to go extinct more possible niches have been formed, facilitating increased diversity. Our model also shows that the inclusion of species utilising near-to-equilibrium reactions increases diversity under conditions of low free-energy supply. At low free energy supply thermodynamic interaction types reach comparable strength to the conventional (competition and facilitation) interactions yielding a more nuanced classification of interactions, and emphasising the key role thermodynamics plays in the dynamics. Though our model is valid for all microbial ecosystems where diversification from an initial substrate occurs it is of particular use when the initial substrate is recalcitrant (low-free energy).

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“…When we do allow for evolutionary changes, ecological coexistence could become destabilized. When the coexistence is stable even under evolutionary changes, I will refer to it as evolutionary coexistence (termed 'evolutionarily stable coexistence' by Li et al 13 ), which leads to an evolutionarily singular coalition (ESC) defined in adaptive dynamics 14 . In this case a coalition is a collection of phenotypes, that can occur through evolutionary branching, but also through e.g.…”

Section: Evolutionary Optimum Evolutionarily Stable Strategies (Ess) and Evolutionarily Singular Coalitions (Esc)mentioning

confidence: 99%

Evolutionary coexistence in a fluctuating environment by specialization on resource level

Wortel

2021

Preprint

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Microbial communities in fluctuating environments can contain many species and diversity within species, both in natural environments such as the human gut, and in laboratory settings when communities are propagated for a long time. Whether this diversity is at the species level, within the species level, or a combination of both, the question remains: what processes lead to the origination and maintenance of this diversity? When nutrient levels fluctuate over time, one possibly relevant process is that different types specialize on low and high nutrient levels. The relevance of this process is supported by observations of types co-existing through this mechanism when put together in the laboratory, and simple models, which show that negative frequency dependence of two types, specialized on low and high resource level, can stabilize coexistence. However, when microbial populations are in an environment for a long time, they will evolve. In this article we determine what happens when species can evolve; whether branching can occur to create diversity and whether evolution will destabilize coexistence. We find that for the trade-off data between growth at low and high substrate concentrations, available for the bacterium Escherichia coli and the yeast Saccharomyces cerevisiae on glucose, there is only a small portion of the trait-space that allows for coexistence. Moreover, this coexistence is destabilized by evolution, and the only evolutionary stable outcome is a single strategy. When we combine two species that are well-adapted on their own, we do find that they can form an evolutionary singular coalition. We conclude that although specialization on resource level can support diversity within a species, it is likely not a cause by itself. In contrast, for species consortia this specialization can lead to evolutionary stable coexistence.

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Modeling microbial metabolic trade-offs in a chemostat

Cited by 34 publications

References 60 publications

Microbial Community Decision Making Models in Batch and Chemostat Cultures

Microbial Community Decision Making Models in Batch and Chemostat Cultures

Thermodynamic constraints on the assembly and diversity of microbial ecosystems are different near to and far from equilibrium

Evolutionary coexistence in a fluctuating environment by specialization on resource level

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