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hal.structure.identifierPleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
hal.structure.identifierDynamics, Logics and Inference for biological Systems and Sequences [Dyliss]
hal.structure.identifierQuadram Institute
dc.contributor.authorFRIOUX, Clémence
hal.structure.identifierLaboratoire de Biologie Intégrative des Modèles Marins [LBI2M]
dc.contributor.authorDITTAMI, Simon
hal.structure.identifierDynamics, Logics and Inference for biological Systems and Sequences [Dyliss]
dc.contributor.authorSIEGEL, Anne
dc.date.issued2020-05-07
dc.identifier.issn0300-5127
dc.description.abstractEnSystems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming , a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states of Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As a first step in this field, we will illustrate how the reduction of microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.
dc.description.sponsorshipBiotechnologies pour la valorisation des macroalgues - ANR-10-BTBR-0004
dc.language.isoen
dc.publisherPortland Press
dc.subject.enGap-filling
dc.subject.enMetabolic networks
dc.subject.enSystems biology
dc.subject.enSystems ecology
dc.subject.enCommunity selection
dc.subject.enNon-model organisms
dc.title.enUsing automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host–microbial interactions
dc.typeArticle de revue
dc.identifier.doi10.1042/BST20190667
dc.subject.halInformatique [cs]/Bio-informatique [q-bio.QM]
dc.subject.halSciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
bordeaux.journalBiochemical Society Transactions
bordeaux.page901-913
bordeaux.volume48
bordeaux.issue3
bordeaux.peerReviewedoui
hal.identifierhal-02569935
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02569935v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Biochemical%20Society%20Transactions&rft.date=2020-05-07&rft.volume=48&rft.issue=3&rft.spage=901-913&rft.epage=901-913&rft.eissn=0300-5127&rft.issn=0300-5127&rft.au=FRIOUX,%20Cl%C3%A9mence&DITTAMI,%20Simon&SIEGEL,%20Anne&rft.genre=article


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