Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction

Saez-Rodriguez, J; Alexopoulos, LG; Epperlein, J; Samaga, R; Lauffenburger, DA; Klamt, S; Sorger, PK

dc.contributor.author	Saez-Rodriguez, J	en
dc.contributor.author	Alexopoulos, LG	en
dc.contributor.author	Epperlein, J	en
dc.contributor.author	Samaga, R	en
dc.contributor.author	Lauffenburger, DA	en
dc.contributor.author	Klamt, S	en
dc.contributor.author	Sorger, PK	en
dc.date.accessioned	2014-03-01T01:30:13Z
dc.date.available	2014-03-01T01:30:13Z
dc.date.issued	2009	en
dc.identifier.issn	17444292	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19501
dc.subject	Logical modelling	en
dc.subject	Protein networks	en
dc.subject	Signal transduction	en
dc.subject.other	cytokine	en
dc.subject.other	protein	en
dc.subject.other	article	en
dc.subject.other	computer program	en
dc.subject.other	controlled study	en
dc.subject.other	cytokine production	en
dc.subject.other	information science	en
dc.subject.other	liver cell	en
dc.subject.other	medical literature	en
dc.subject.other	priority journal	en
dc.subject.other	protein analysis	en
dc.subject.other	protein function	en
dc.subject.other	signal transduction	en
dc.subject.other	Mammalia	en
dc.title	Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction	en
heal.type	journalArticle	en
heal.identifier.primary	10.1038/msb.2009.87	en
heal.identifier.secondary	331	en
heal.identifier.secondary	http://dx.doi.org/10.1038/msb.2009.87	en
heal.publicationDate	2009	en
heal.abstract	Large-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. © 2009 EMBO and Macmillan Publishers Limited. All rights reserved.	en
heal.journalName	Molecular Systems Biology	en
dc.identifier.doi	10.1038/msb.2009.87	en
dc.identifier.volume	5	en