Show simple item record

dc.contributor.authorSaez-Rodriguez, Julio
dc.contributor.authorAlexopoulos, Leonidas G
dc.contributor.authorEpperlein, Jonathan
dc.contributor.authorSamaga, Regina
dc.contributor.authorLauffenburger, Douglas A
dc.contributor.authorKlamt, Steffen
dc.contributor.authorSorger, Peter Karl
dc.date.accessioned2013-01-28T18:36:44Z
dc.date.issued2009
dc.identifier.citationSaez-Rodriguez, Julio, Leonidas G. Alexopoulos, Jonathan Epperlein, Regina Samaga, Douglas A. Lauffenburger, Steffen Klamt, and Peter K. Sorger. 2009. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Molecular Systems Biology 5:331.en_US
dc.identifier.issn1744-4292en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10235326
dc.description.abstractLarge-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks.en_US
dc.language.isoen_USen_US
dc.publisherNature Publishing Groupen_US
dc.relation.isversionofdoi://10.1038/msb.2009.87en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2824489/pdf/en_US
dash.licenseLAA
dc.subjectlogical modelingen_US
dc.subjectprotein networksen_US
dc.subjectsignal transductionen_US
dc.titleDiscrete Logic Modelling as a Means to Link Protein Signalling Networks with Functional Analysis of Mammalian Signal Transductionen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalMolecular Systems Biologyen_US
dash.depositing.authorSorger, Peter Karl
dc.date.available2013-01-28T18:36:44Z
dash.affiliation.otherHMS^Systems Biologyen_US
dc.identifier.doi10.1038/msb.2009.87*
dash.contributor.affiliatedSorger, Peter


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record