Mechanistic models of microbial community metabolism

Dillard, Lillian; Payne, Dawson; Papin, Jason A.

doi:10.1039/d0mo00154f

Cited by 24 publications

(18 citation statements)

References 77 publications

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“…In terms of T2D and metabolic modeling of microbiota through annotated genomes, the information is scarce but continues to growth with several articles. 165 For example, Rosario et al used the COBRA Toolbox to model the contribution of four bacteria ( Escherichia spp, Akkermansia muciniphila, Subdoligranulum variabile, and Intestinibacter bartletti ) to the physiology of T2D patients undergoing metformin treatment. 166 To characterize the metabolic alterations produced by this dysbiosis, they applied flux balance analysis (FBA) coupled with synthetic lethality analysis interactions to identify patterns of growth.…”

Section: Systems Biology: In Silico Modeling Of Metabolism In Gut Mic...mentioning

confidence: 99%

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

et al. 2022

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The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.

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Section: Systems Biology: In Silico Modeling Of Metabolism In Gut Mic...mentioning

confidence: 99%

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

et al. 2022

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show abstract

“…Statistical analyses (e.g., regression, classification) can then be performed using these traits as input to predict environmental observations (e.g., nutrient abundances, plant yields, clinical outcomes) ( 17 ). These applications, as well as their use across a variety of systems and temporospatial scales, have been thoroughly reviewed ( 18 – 21 ).…”

Section: Opportunitiesmentioning

confidence: 99%

“…First, different chemical and reaction namespaces between disparate data sets and models can hinder the combination of GEMs from different sources ( 44 , 45 ). Additionally, community FBA methods require the formulation of a community-level objective that both draws from biological rationales and balances the trade-off between community-level and individual objectives, making it difficult to accurately predict phenotypes for complex microbiomes ( 21 , 46 ).…”

Section: Challengesmentioning

confidence: 99%

Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

Ankrah

Bernstein

Biggs³

et al. 2021

mSystems

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“…Constraint-based modelling of genome-scale metabolic networks provides the means to in silico analyse microbial community interactions [ 8 – 12 ]. The existing metabolic reconstruction approaches [ 13 – 19 ] rely on linking genome annotation to enzymatic reactions from various databases [ 19 – 23 ].…”

Section: Introductionmentioning

confidence: 99%

COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions

Wendering

Nikoloski

2022

PLoS Comput Biol

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Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.

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Mechanistic models of microbial community metabolism

Abstract: Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation.

Cited by 24 publications

References 77 publications

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions

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