Metabolic Games

Pusa, Taneli; Wannagat, Martin; Sagot, Marie‐France

doi:10.3389/fams.2019.00018

Cited by 10 publications

(10 citation statements)

References 66 publications

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“…To incorporate FBA models in the chemostat community model frame work, due to the internal degrees of freedom of FBA models, the fundamentally game theoretic problem of multiple decision makers has to be taken into account [24]. Here, and in line we previous proposals for community modeling, we therefore explored two flavors: rational agent and rational community.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in d-OptCom, an influential method for dFBA of a community of GSMs, decisions are based on a community objective (high community biomass production) as well as individual objectives (high growth rate) [29]. In other methods, the emergence of multiple decision makers has stimulated the use of game theory for the analysis of microbial interactions [24].…”

Section: Introductionmentioning

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: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microbial Community Decision Making Models in Batch and Chemostat Cultures

Theorell

Stelling

2021

Computational Methods in Systems Biology

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“…Evolutionary game theory (EGT) can also be used to model microbiome population dynamics (as described in 109,113,114). The fitness parameters that dictate the outcome of metabolic games in microbiomes can be difficult to estimate, as they are influenced by nonlinear environmental and intracellular conditions.…”

Section: Ordinary Differential Equation and Evolutionary Game Theory Models Of Microbiome Population Dynamics And Interactionsmentioning

confidence: 99%

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes

Leggieri

Liu

Hayes

et al. 2021

Annu. Rev. Biomed. Eng.

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Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review,we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 23 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…Even though we have presented five modeling frameworks as distinct and complementary, hybrid approaches that combine two or more frameworks have been proposed and are becoming increasingly more popular. For example, network models that simulate generalized Lotka-Volterra dynamics have been considered extensively in the literature on the stability-diversity relationship [123,[133][134][135][136] and game theoretic, network approaches that also include information from metabolic analysis have been developed [137,138]. Ultimately, the choice of modeling framework depends on biological and biochemical knowledge available for the system of interest, and on the study question at hand.…”

Section: Mathematical Modeling Of Microbial Communitiesmentioning

confidence: 99%

Microfluidic and mathematical modeling of aquatic microbial communities

Liu

Giometto

2020

Anal Bioanal Chem

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Aquatic microbial communities contribute fundamentally to biogeochemical transformations in natural ecosystems, and disruption of these communities can lead to ecological disasters such as harmful algal blooms. Microbial communities are highly dynamic, and their composition and function are tightly controlled by the biophysical (e.g., light, fluid flow, and temperature) and biochemical (e.g., chemical gradients and cell concentration) parameters of the surrounding environment. Due to the large number of environmental factors involved, a systematic understanding of the microbial community-environment interactions is lacking. In this article, we show that microfluidic platforms present a unique opportunity to recreate well-defined environmental factors in a laboratory setting in a high throughput way, enabling quantitative studies of microbial communities that are amenable to theoretical modeling. The focus of this article is on aquatic microbial communities, but the microfluidic and mathematical models discussed here can be readily applied to investigate other microbiomes.

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Metabolic Games

Cited by 10 publications

References 66 publications

Microbial Community Decision Making Models in Batch and Chemostat Cultures

Microbial Community Decision Making Models in Batch and Chemostat Cultures

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes

Microfluidic and mathematical modeling of aquatic microbial communities

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