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Forme contrainte

hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
hal.structure.identifierPleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
dc.contributor.authorTATHO DJEANOU, Sthyve Junior
hal.structure.identifierInstitut Sophia Agrobiotech [ISA]
hal.structure.identifierModélisation et commande de systèmes biologiques et écologiques [MACBES]
dc.contributor.authorBALDAZZI, Valentina
hal.structure.identifierPleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
hal.structure.identifierUniversité Côte d'Azur [UniCA]
dc.contributor.authorLABARTHE, Simon
dc.description.abstractEnUnderstanding microbial community functions is challenging due to complex interactions and assembly mechanisms. However, advances in sequencing technologies have enabled the collection of multi-omics data, including population counts and metabolomic or metatranscriptomic profiles. Our main objective is to develop a mathematical model capable of integrating time series of multi-omics data at the community scale.We introduce the community Metabolic Flux Analysis (cMFA) method: a biology-informed inference approach that generalizes classical Metabolic Flux Analysis. This high-dimensional analytical framework aims to estimate metabolic fluxes by integrating multi-omics data. Specifically, we aim to (i) quantify, for each member of the microbial community, their individual contributions to overall community dynamics based on external measurements of metabolite dynamics, and (ii) infer their intracellular distribution of metabolic fluxes. The difficulty here is in accurately inferring latent internal rates from a few observations of community-scale consumption and production rates for extracellular metabolites.We evaluated the cMFA method using synthetic data generated from dynamic models of microbial communities of increasing complexity using dynamic flux balance analysis, based on metabolic models of different Escherichia coli mutants. Synthetic metatranscriptomic data were obtained from internal metabolic fluxes simulated in the dynamic model. To assess the robustness of the method, we benchmarked its performance under varying levels of experimental noise.
dc.description.sponsorshipComputationel models of crop plant microbial biodiversity - ANR-22-PEAE-0011
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enDynamical system
dc.subject.enApplied mathematics
dc.subject.enInference
dc.subject.enBiological system
dc.titleForme contrainte
dc.title.enInference Method cMFA for multi-omics data integration in microbial community models
dc.title.enConstrained form
dc.typeDocument de travail - Pré-publication
dc.subject.halInformatique [cs]/Bio-informatique [q-bio.QM]
dc.subject.halMathématiques [math]
hal.identifierhal-05105798
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-05105798v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.title=Forme%20contrainte&rft.atitle=Forme%20contrainte&rft.au=TATHO%20DJEANOU,%20Sthyve%20Junior&BALDAZZI,%20Valentina&LABARTHE,%20Simon&rft.genre=preprint


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