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

TitleDiscrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction.
Publication TypeJournal Article
Year of Publication2009
AuthorsSaez-Rodriguez, J, Alexopoulos, LG, Epperlein, J, Samaga, R, Lauffenburger, DA, Klamt, S, Sorger, PK
JournalMol Syst Biol
Volume5
Pagination331
Date Published2009
ISSN1744-4292
KeywordsComputational Biology, Cytokines, Humans, Liver, Models, Biological, Proteins, Signal Transduction, Software
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.

DOI10.1038/msb.2009.87
Alternate JournalMol. Syst. Biol.
PubMed ID19953085
PubMed Central IDPMC2824489
Grant ListP50 GM068762-07 / GM / NIGMS NIH HHS / United States
P50-GM68762 / GM / NIGMS NIH HHS / United States
U54-CA112967 / CA / NCI NIH HHS / United States