Scalable and exhaustive screening of metabolic functions carried out by microbial consortia

Clémence Frioux 1 Enora Fremy 1 Camille Trottier 2 Anne Siegel 1
1 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences
Inria Rennes – Bretagne Atlantique , IRISA_D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
2 COMBI - Combinatoire et Bioinformatique
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : Motivation The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far. Results We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria.
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Article dans une revue
Bioinformatics, Oxford University Press (OUP), 2018, 34 (17), pp.i934 - i943. 〈10.1093/bioinformatics/bty588〉
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Contributeur : Clémence Frioux <>
Soumis le : mardi 11 septembre 2018 - 10:00:05
Dernière modification le : jeudi 15 novembre 2018 - 11:59:01

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Clémence Frioux, Enora Fremy, Camille Trottier, Anne Siegel. Scalable and exhaustive screening of metabolic functions carried out by microbial consortia. Bioinformatics, Oxford University Press (OUP), 2018, 34 (17), pp.i934 - i943. 〈10.1093/bioinformatics/bty588〉. 〈hal-01871600〉

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