MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

Diener, Christian; Gibbons, Sean M.; Resendis‐Antonio, Osbaldo

doi:10.1128/msystems.00606-19

Cited by 177 publications

(226 citation statements)

References 61 publications

(66 reference statements)

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“…Ongoing research is working on identifying and annotating novel human microbiome species which is needed as a basis for metabolic modeling. Furthermore, once models are generated, details on material (protein, metabolite) exchange and appropriate community objective functions need to be considered [6,21,36]. Advances in this manner will aid in understanding the integrated function of the microbiome (interactions and temporal dynamics) to give a better perspective of the functional microbiome [26,28,35].…”

Section: Perspectives and Future Workmentioning

confidence: 99%

“…Disease, diet, geography, lifestyle and even occupation are responsible for giving a unique gut microbiome for every individual as these factors hugely alter the microbiome [48,60,66]. These alterations happening in this large-scale system can be explored only by investigating the metabolic interactions among the microbes, microbe and diet/host and for that, community-based genome scale metabolic modeling may help once annotated species are identified [27,36]. Furthermore, the gut microbiome is involved in many biological functions like carbohydrate metabolism, colonization resistance, and ROS production during cancer [39,53,64].…”

Section: Perspectives and Future Workmentioning

confidence: 99%

“…People, however, have constantly made efforts to tackle this problem by designing constraint based/genome scale modeling software packages. A python software tool called Micom was developed which can take into consideration the objective functions for both individual species and the microbial community including~100 species at a time [36]. Baldini et al have created a MATLAB based software called Microbiome Modeling Toolbox which applies genome scale modeling to simulate pairwise microbe-microbe and human-microbe interactions [27].…”

Section: Introductionmentioning

confidence: 99%

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Computational Modeling of the Human Microbiome

Chowdhury

Fong

2020

Microorganisms

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The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.

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Section: Perspectives and Future Workmentioning

confidence: 99%

Section: Perspectives and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computational Modeling of the Human Microbiome

Chowdhury

Fong

2020

Microorganisms

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“…In summary, most of the studies described above are static, but allow for large and potentially complex microbial communities (hundreds or thousands of strains) [34,30,35,40]. In contrast, dynamic methods, including the MDP-biomeGEM approach described here, are only capable of modelling smaller, simpler population structures (approx.…”

Section: Comparison With Peer Approachesmentioning

confidence: 99%

Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering

García-Jiménez

Carrasco

Medina

et al. 2020

Preprint

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Background : There are few large longitudinal microbiome studies, and fewer that include controlled, well-annotated perturbations between sampling-points. Thus, there are few opportunities to employ data-driven computational analyses of perturbed microbial communities over time. Results : Our novel computational system simulates the dynamics of microbial communities under perturbations using genome-scale metabolic models (GEMs). Perturbations include modifications to a) the nutrients available in the medium, allowing modelling of prebiotics; and/or b) the microorganisms present in the community to model, for example, probiotics or pathogen infection. These simulations generate the quantity and types of information required by MDPbiome, an AI system which builds predictive models suggesting the perturbation(s) required to engineer microbial communities to a desired state. We call this novel combination of technologies "MDPbiomeGEM"'. We demonstrate, in a Crohn’s disease microbiome, that MDPbiomeGEM correctly models the influence of both prebiotic fiber and a probiotic, resulting in a recommendation to consume inulin to recover from dysbiosis, consistent with prior biomedical knowledge. When used to model the soil microbiome's ability to degrade the herbicide Atrazine, differing recommendations arise depending on the highly variable state of the initial soil microbial composition, highlighting the relevance of both phosphate and microbes (i.e. Halobacillus sp. and H.stevensii ) in a directed microbiome engineering strategy, consistent with previously published observations. Conclusions : MDPbiomeGEM generates large volumes of longitudinal data of complex microbial communities experiencing perturbations. Machine learning on these data reveal patterns consistent with existing biological knowledge, supporting the validity of the approach. MDPbiomeGEM could save research resources by optimizing sample collection in metagenomics studies through identification of "informative" scenarios/time-points, or by predicting optimal in-vitro culture formulations for generating performant synthetic microbial communities. Finally, MDPbiomeGEM outputs include detailed information about the metabolic state of the community, which can be used to further interpret the impact of perturbations, and potentially could be used to predict novel metabolic biomarkers of a microbiome's state.

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“…Michaelis-Menten or Hill kinetics) are a particularly common choice [32,28,29]. We show that a simpler quadratic species-metabolite interaction (QSMI) model can recapitulate the growth experiment outcomes of Friedman et al [16] with a single parameterization.It is worth noting explicitly that our work does not explore the accuracy of specific modeling tools [33,34,35,36], but instead examines whether the mathematical formulation of the model could ever be used to recapitulate the biological dynamics. A positive answer means that the mathematical form of the model can potentially be useful, indicating promise for future endeavors, while a negative answer indicates that the basic formulation of the model is inappropriate for predictive models of microbial communities.…”

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confidence: 99%

Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species–species modelling

Brunner

Chia

2019

J. R. Soc. Interface.

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Personalized models of the gut microbiome are valuable for disease prevention and treatment. For this, one requires a mathematical model that predicts microbial community composition and the emergent behaviour of microbial communities. We seek a modelling strategy that can capture emergent behaviour when built from sets of universal individual interactions. Our investigation reveals that species–metabolite interaction (SMI) modelling is better able to capture emergent behaviour in community composition dynamics than direct species–species modelling. Using publicly available data, we examine the ability of species–species models and species–metabolite models to predict trio growth experiments from the outcomes of pair growth experiments. We compare quadratic species–species interaction models and quadratic SMI models and conclude that only species–metabolite models have the necessary complexity to explain a wide variety of interdependent growth outcomes. We also show that general species–species interaction models cannot match the patterns observed in community growth dynamics, whereas species–metabolite models can. We conclude that species–metabolite modelling will be important in the development of accurate, clinically useful models of microbial communities.

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MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

Cited by 177 publications

References 61 publications

Computational Modeling of the Human Microbiome

Computational Modeling of the Human Microbiome

Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering

Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species–species modelling

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