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dc.contributor.authorSzederkényi, Gábor-
dc.contributor.authorBanga, Julio R.-
dc.contributor.authorAlonso, Antonio A.-
dc.date.accessioned2013-01-22T08:59:28Z-
dc.date.available2013-01-22T08:59:28Z-
dc.date.issued2012-
dc.identifier.citationBioinformatics 28(11): 1549-1550 (2012)es_ES
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10261/64629-
dc.description2 páginas, 1 figuraes_ES
dc.description.abstractChemical reaction network theory is widely used in modeling and analyzing complex biochemical systems such as metabolic networks and cell signalling pathways. Being able to produce all the biologically and chemically important qualitative dynamical features, chemical reaction networks (CRNs) have attracted significant attention in the systems biology community. It is well-known that the reliable inference of CRN models generally requires thorough identifiability and distinguishability analysis together with carefully selected prior modeling assumptions. Here, we present a software toolbox CRNreals that supports the distinguishability and identifiability analysis of CRN models using recently published optimization-based procedures.es_ES
dc.description.sponsorshipSpanish MICINN project ‘MultiScales’ (DPI2011-28112-C04-03) and CSIC intramural project ‘BioREDES’ (ref. PIE-201170E018) and Hungarian Scientific Research Fund (grant no. 83440) for G. S.es_ES
dc.language.isoenges_ES
dc.publisherOxford University Presses_ES
dc.rightsclosedAccesses_ES
dc.titleCRNreals: A toolbox for distinguishability and identifiability analysis of biochemical reaction networkses_ES
dc.typeartículoes_ES
dc.identifier.doi10.1093/bioinformatics/bts171-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1093/bioinformatics/bts171es_ES
dc.identifier.e-issn1460-2059-
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