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dc.contributor.authorIsik, Z.en_US
dc.contributor.authorErsahin, T.en_US
dc.contributor.authorAtalay V.en_US
dc.contributor.authorAykanat, C.en_US
dc.contributor.authorCetin-Atalay, R.en_US
dc.date.accessioned2016-02-08T09:49:15Z
dc.date.available2016-02-08T09:49:15Z
dc.date.issued2012en_US
dc.identifier.issn1742206Xen_US
dc.identifier.urihttp://hdl.handle.net/11693/21648
dc.description.abstractDetermination of cell signalling behaviour is crucial for understanding the physiological response to a specific stimulus or drug treatment. Current approaches for large-scale data analysis do not effectively incorporate critical topological information provided by the signalling network. We herein describe a novel model- and data-driven hybrid approach, or signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signalling pathways that lead to ultimate cell responses such as survival, migration or death. This score flow algorithm translates signalling pathways as a directed graph and maps experimental data, including negative and positive feedbacks, onto gene nodes as scores, which then computationally traverse the signalling pathway until a pre-defined biological target response is attained. Initially, experimental data-driven enrichment scores of the genes were computed in a pathway, then a heuristic approach was applied using the gene score partition as a solution for protein node stoichiometry during dynamic scoring of the pathway of interest. Incorporation of a score partition during the signal flow and cyclic feedback loops in the signalling pathway significantly improves the usefulness of this model, as compared to other approaches. Evaluation of the score flow algorithm using both transcriptome and ChIP-seq data-generated signalling pathways showed good correlation with expected cellular behaviour on both KEGG and manually generated pathways. Implementation of the algorithm as a Cytoscape plug-in allows interactive visualization and analysis of KEGG pathways as well as user-generated and curated Cytoscape pathways. Moreover, the algorithm accurately predicts gene-level and global impacts of single or multiple in silico gene knockouts. © The Royal Society of Chemistry 2012.en_US
dc.language.isoEnglishen_US
dc.source.titleMolecular BioSystemsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1039/c2mb25215een_US
dc.subjecttranscriptomeen_US
dc.subjectalgorithmen_US
dc.subjectarticleen_US
dc.subjectbiological modelen_US
dc.subjectbiologyen_US
dc.subjectgene expression profilingen_US
dc.subjectprotein microarrayen_US
dc.subjectsignal transductionen_US
dc.subjectAlgorithmsen_US
dc.subjectComputational Biologyen_US
dc.subjectGene Expression Profilingen_US
dc.subjectModels, Biologicalen_US
dc.subjectProtein Array Analysisen_US
dc.subjectSignal Transductionen_US
dc.subjectTranscriptomeen_US
dc.titleA signal transduction score flow algorithm for cyclic cellular pathway analysis, which combines transcriptome and ChIP-seq dataen_US
dc.typeArticleen_US
dc.departmentDepartment of Molecular Biology and Genetics
dc.departmentDepartment of Computer Engineering
dc.citation.spage3224en_US
dc.citation.epage3231en_US
dc.citation.volumeNumber8en_US
dc.citation.issueNumber12en_US
dc.identifier.doi10.1039/c2mb25215een_US
dc.publisherRoyal Society of Chemistryen_US


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