Show simple item record

dc.contributor.authorGuillén Gosálbez, Gonzalo
dc.contributor.authorMiró, Antoni
dc.contributor.authorAlves, Rui
dc.contributor.authorSorribas Tello, Albert
dc.contributor.authorJiménez Esteller, Laureano
dc.date.accessioned2015-06-02T11:05:51Z
dc.date.available2015-06-02T11:05:51Z
dc.date.issued2013
dc.identifier.issn1752-0509
dc.identifier.urihttp://hdl.handle.net/10459.1/48285
dc.description.abstractBackground: Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality. Results: Here, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixedinteger dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions. Conclusion: The capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters.ca_ES
dc.description.sponsorshipThe authors acknowledges the financial support of the following institutions: Spanish Ministry of Education and Science (CTQ2009-14420-C02, CTQ2012-37039-C02, DPI2012-37154-C02-02, BFU2008-00196/BMC, BFU2010-17704, SGR2009-0809 and ENE 2011-28269-CO3-03), Spanish Ministry of External Affairs (projects PHB 2008-0090-PC), and European Commission (Marie Curie Actions - IAPP program - FP7/251298).
dc.language.isoengca_ES
dc.publisherBioMed Centralca_ES
dc.relationMICINN/PN2008-2011/CTQ2009-14420-C02
dc.relationMICINN/PN2008-2011/CTQ2012-37039-C02
dc.relationMICINN/PN2008-2011/DPI2012-37154-C02-02
dc.relationMICINN/PN2008-2011/BFU2008-00196/BMC
dc.relationMICINN/PN2008-2011/BFU2010-17704
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1186/1752-0509-7-113ca_ES
dc.relation.ispartofBMC Systems Biology, 2013, vol. 7, núm. 113, p. 1-11ca_ES
dc.rightscc-by, (c) Guillén Gosálbez et al., 2013ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectParameter estimationca_ES
dc.subjectStructure identificationca_ES
dc.subjectAkaike criterionca_ES
dc.titleIdentification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimizationca_ES
dc.typearticleca_ES
dc.identifier.idgrec020207
dc.type.versionpublishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1186/1752-0509-7-113
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/251298


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

cc-by, (c) Guillén Gosálbez et al., 2013
Except where otherwise noted, this item's license is described as cc-by, (c) Guillén Gosálbez et al., 2013