Document Detail


A signal transduction score flow algorithm for cyclic cellular pathway analysis, which combines transcriptome and ChIP-seq data.
MedLine Citation:
PMID:  23042589     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Determination 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.
Authors:
Zerrin Isik; Tulin Ersahin; Volkan Atalay; Cevdet Aykanat; Rengul Cetin-Atalay
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-10-8
Journal Detail:
Title:  Molecular bioSystems     Volume:  -     ISSN:  1742-2051     ISO Abbreviation:  Mol Biosyst     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-10-8     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101251620     Medline TA:  Mol Biosyst     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Bioinformatics, Biotechnology Center, Technical University Dresden, 01307 Dresden, Germany. zerrin.isik@biotec.tu-dresden.de.
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