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Scalable and exhaustive screening of metabolic functions carried out by microbial consortia
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 scal-able 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 benchmark-ing 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. Availability and implementation: Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto.
Cited literature [42 references]
https://hal.inria.fr/hal-01946860
Contributor : Clémence Frioux Connect in order to contact the contributor
Submitted on : Tuesday, January 8, 2019 - 12:04:04 PM
Last modification on : Wednesday, January 19, 2022 - 3:48:24 PM
Long-term archiving on: : Tuesday, April 9, 2019 - 3:22:27 PM
Contributor : Clémence Frioux Connect in order to contact the contributor
Submitted on : Tuesday, January 8, 2019 - 12:04:04 PM
Last modification on : Wednesday, January 19, 2022 - 3:48:24 PM
Long-term archiving on: : Tuesday, April 9, 2019 - 3:22:27 PM
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Frioux et al. - 2018 - Scalabl...
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