Automated Modelling of Signal Transduction Networks

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Automated Modelling of Signal Transduction Networks

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dc.contributor.author Steffen, Martin
dc.contributor.author Petti, Allegra
dc.contributor.author D'haeseleer, Patrik
dc.contributor.author Aach, John Dennis
dc.contributor.author Church, George McDonald
dc.date.accessioned 2011-03-10T00:28:09Z
dc.date.issued 2002
dc.identifier.citation Steffen, Martin, Allegra Petti, John Aach, Patrik D'haeseleer, and George Church. 2002. Automated modelling of signal transduction networks. BMC Bioinformatics 3: 34. en_US
dc.identifier.issn 1471-2105 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4740118
dc.description.abstract Background: Intracellular signal transduction is achieved by networks of proteins and small molecules that transmit information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. Understanding the mechanisms cells use to accomplish this important process requires a detailed molecular description of the networks involved. Results: We have developed a computational approach for generating static models of signal transduction networks which utilizes protein-interaction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles. Conclusion: We show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.isversionof http://www.biomedcentral.com/1471-2105/3/34 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC137599/pdf/ en_US
dash.license LAA
dc.title Automated Modelling of Signal Transduction Networks en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal BMC Bioinformatics en_US
dash.depositing.author Aach, John Dennis
dc.date.available 2011-03-10T00:28:09Z
dash.affiliation.other HMS^Genetics en_US
dash.affiliation.other HMS^Health Sciences and Technology en_US
dash.affiliation.other HMS^Genetics en_US

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