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Seed2LP: seed inference in metabolic networks for reverse ecology applications

hal.structure.identifierPleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorGHASSEMI NEDJAD, Chabname
hal.structure.identifierLaboratoire des Sciences du Numérique de Nantes [LS2N]
hal.structure.identifierCombinatoire et Bioinformatique [LS2N - équipe COMBI]
hal.structure.identifierNantes Université [Nantes Univ]
dc.contributor.authorBOLTEAU, Mathieu
hal.structure.identifierCentre Hospitalier Régional Universitaire de Brest [CHRU Brest]
dc.contributor.authorBOURNEUF, Lucas
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorPAULEVE, Loic
hal.structure.identifierPleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
dc.contributor.authorFRIOUX, Clémence
dc.date.issued2024-09-27
dc.description.abstractEnA challenging problem in microbiology is to determine nutritional requirements of microorganisms and culture them, especially for the microbial dark matter detected solely with culture-independent methods. The latter foster an increasing amount of genomic sequences that can be explored with reverse ecology approaches to raise hypotheses on the corresponding populations. Building upon genome scale metabolic networks (GSMNs) obtained from genome annotations, metabolic models predict contextualised phenotypes using nutrient information. We developed the tool Seed2LP, addressing the inverse problem of predicting source nutrients, or seeds, from a GSMN and a metabolic objective. The originality of Seed2LP is its hybrid model, combining a scalable and discrete Boolean approximation of metabolic activity, with the numerically accurate flux balance analysis (FBA). Seed inference is highly customisable, with multiple search and solving modes, exploring the search space of external and internal metabolites combinations. Application to a benchmark of 107 curated GSMNs highlights the usefulness of a logic modelling method over a graph-based approach to predict seeds, and the relevance of hybrid solving to satisfy FBA constraints. Focusing on the dependency between metabolism and environment, Seed2LP is a computational support contributing to address the multifactorial challenge of culturing possibly uncultured microorganisms. Seed2LP is available on https://github.com/bioasp/seed2lp.
dc.description.sponsorshipComputationel models of crop plant microbial biodiversity - ANR-22-PEAE-0011
dc.description.sponsorshipAbstraction des Réseaux de Réactions vers des Réseaux Booléens pour Améliorer l'Inférence et le Contrôle en Biologie des Systèmes - ANR-23-CE45-0008
dc.description.sponsorshipShared CULTuromics platform to IncreaSe acceSs to the vast number of microorganisms, some yet uncultivated, to understand the key functIons and services to huMan ecosystem of MicrobiOmes - ANR-24-PESA-0002
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enMetabolic modelling
dc.subject.engrowth medium inference
dc.subject.enuncultured bacteria
dc.subject.enreverse ecology
dc.subject.ennetwork expansion
dc.subject.enconstraint-based modelling
dc.title.enSeed2LP: seed inference in metabolic networks for reverse ecology applications
dc.typeDocument de travail - Pré-publication
dc.identifier.doi10.1101/2024.09.26.615309
dc.subject.halInformatique [cs]/Bio-informatique [q-bio.QM]
dc.subject.halSciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
hal.identifierhal-04713829
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04713829v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2024-09-27&rft.au=GHASSEMI%20NEDJAD,%20Chabname&BOLTEAU,%20Mathieu&BOURNEUF,%20Lucas&PAULEVE,%20Loic&FRIOUX,%20Cl%C3%A9mence&rft.genre=preprint


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