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hal.structure.identifierModels and Algorithms for the Genome [ MAGNOME]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorZHUKOVA, Anna
hal.structure.identifierModels and Algorithms for the Genome [ MAGNOME]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorSHERMAN, David James
dc.date.accessioned2024-04-15T09:43:03Z
dc.date.available2024-04-15T09:43:03Z
dc.date.issued2013-07-26
dc.date.conference2013-07-25
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/197732
dc.description.abstractEnGenome-scale metabolic networks are complex systems that describe thousands of reactions participating in the organism's metabolism. During the process of genome network reconstruction these reactions are automatically inferred from pathway and reaction databases, and existing models for similar organisms, using genomic data [1]. The inferred draft network is then refined during several iterations of error detection, gap filling, analysis and improvement [2]. Although automatic tools for model inference and analysis are becoming more and more powerful, they may still miss some reactions or add those ones that should not belong to the network of the target organism. That is why the analysis by a human expert is needed during the network refinement process. However, being tailored for a computer simulation, and thus including all the reactions thought to participate in the organism's metabolism, genome-scale networks can be too complicated and detailed for a human. The errors may be hidden in the multitude of reactions. To help a human expert understanding these detailed networks, we developed a method for knowledge-based generalization that focusses on the higher-level relationships in the network, while omitting the details [3]. The generalization process groups chemical species present in the network into semantically equivalent classes, based on their hierarchical relationships in the ChEBI ontology [4], and merges them into a generalized chemical species. For instance, butyryl-CoA, hexanoyl-CoA and octanoyl-CoA species can be generalized into fatty acyl-CoA. After the species generalization, reactions that share the same generalized reactants and the same generalized products, are factored together into a generalized reaction. This provides a higher-level view of the network. In this poster, we show the application of this generalization method to the network of the yeast Y. lypolitica [5]. We analyze the generalized network, and illustrate how it can be used for easier error detection: We show the changes that both initial and generalized networks undergo if the catalyzing enzyme for some of the reactions is missing. The application of the generalization procedure also facilitates network comparison, which we show by comparing the generalized Y. lypolitica network to the networks of several other organisms.
dc.language.isoen
dc.subject.enbioinformatics
dc.subject.enmetabolic models
dc.subject.enknowledge engineering
dc.title.enKnowledge-based generalization of metabolic networks: An applicational study
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]/Bio-informatique [q-bio.QM]
dc.subject.halSciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleMoscow Conference on Computational Molecular Biology
bordeaux.countryRU
bordeaux.conference.cityMoscow
bordeaux.peerReviewedoui
hal.identifierhal-00859440
hal.version1
hal.invitednon
hal.proceedingsnon
hal.conference.end2013-07-28
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00859440v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2013-07-26&rft.au=ZHUKOVA,%20Anna&SHERMAN,%20David%20James&rft.genre=unknown


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