Modeling recovery of Crohn's disease, by simulating microbial community dynamics under perturbations

Modeling recovery of Crohn's disease, by simulating microbial community dynamics under perturbations. Carrasco Muriel, J., García-Jiménez, B., & Wlikinson, M., D. In Intelligent Systems for Molecular Biology and European Conference on Computational Biology, 2019.

Website doi abstract bibtex

There are few large longitudinal microbiome studies, and fewer that include planned, annotated perturbations between sampling-points. Thus, there are few opportunities to employ data-driven computational analyses of perturbed microbial communities over time. Our novel computational system simulates the dynamics of microbial communities under perturbations, using genome-scale metabolic models (GEM). Perturbations include modifications to a) the nutrients available in the medium, allowing modelling of prebiotics; or b) the microorganisms present in the community, to model probiotics or pathogen infection. These simulations generate the quantity and types of information used as input to the MDPbiome system, which builds predictive models suggesting the perturbation(s) required to engineer microbial communities to a desired state. We demonstrate that this novel combination, called MDPbiomeGEM, is able to model the influence of prebiotic fiber and probiotic in the case of a Crohn's disease microbiome. The output's recommended perturbation to recover from dysbiosis is to consume inulin, which promotes butyrate production to reach homeostasis, consistent with prior biomed-ical knowledge. Our system could also contribute to design (perturbed) microbial community dynamics experiments, potentially saving resources both in natural microbiome scenarios by optimizing sequencing sampling, or to optimize in-vitro culture formulations for generating performant synthetic microbial communities.

@inproceedings{
 title = {Modeling recovery of Crohn's disease, by simulating microbial community dynamics under perturbations},
 type = {inproceedings},
 year = {2019},
 websites = {https://doi.org/10.6084/m9.figshare.8864426,10.7490/f1000research.1117333,https://www.youtube.com/watch?v=xqUxwYZkn6w&list=PLmX8XnLr6zeHVOUbY0v6Jo22tUGpQSziV&index=5},
 city = {Basel},
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 created = {2019-09-30T15:48:41.634Z},
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 last_modified = {2019-09-30T15:48:41.634Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {There are few large longitudinal microbiome studies, and fewer that include planned, annotated perturbations between sampling-points. Thus, there are few opportunities to employ data-driven computational analyses of perturbed microbial communities over time. Our novel computational system simulates the dynamics of microbial communities under perturbations, using genome-scale metabolic models (GEM). Perturbations include modifications to a) the nutrients available in the medium, allowing modelling of prebiotics; or b) the microorganisms present in the community, to model probiotics or pathogen infection. These simulations generate the quantity and types of information used as input to the MDPbiome system, which builds predictive models suggesting the perturbation(s) required to engineer microbial communities to a desired state. We demonstrate that this novel combination, called MDPbiomeGEM, is able to model the influence of prebiotic fiber and probiotic in the case of a Crohn's disease microbiome. The output's recommended perturbation to recover from dysbiosis is to consume inulin, which promotes butyrate production to reach homeostasis, consistent with prior biomed-ical knowledge. Our system could also contribute to design (perturbed) microbial community dynamics experiments, potentially saving resources both in natural microbiome scenarios by optimizing sequencing sampling, or to optimize in-vitro culture formulations for generating performant synthetic microbial communities.},
 bibtype = {inproceedings},
 author = {Carrasco Muriel, Jorge and García-Jiménez, Beatriz and Wlikinson, Mark D},
 doi = {10.13140/RG.2.2.33350.63049},
 booktitle = {Intelligent Systems for Molecular Biology and European Conference on Computational Biology}
}

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