Construction of signaling pathways and identification of drug effects on the liver cancer cell HepG2

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dc.contributor.author Alexopoulos, LG en
dc.contributor.author Melas, IN en
dc.contributor.author Chairakaki, AD en
dc.contributor.author Saez-Rodriguez, J en
dc.contributor.author Mitsos, A en
dc.date.accessioned 2014-03-01T02:46:43Z
dc.date.available 2014-03-01T02:46:43Z
dc.date.issued 2010 en
dc.identifier.issn 1557170X en
dc.identifier.uri http://hdl.handle.net/123456789/32812
dc.subject Computer Model en
dc.subject Drug Effects en
dc.subject Functional Analysis en
dc.subject Gene Expression en
dc.subject Integer Linear Program en
dc.subject Literature Search en
dc.subject Liver Cancer en
dc.subject Pharmaceutical Industry en
dc.subject Signaling Pathway en
dc.subject Text Mining en
dc.subject High Throughput en
dc.subject.other Anticancer drug en
dc.subject.other Cell types en
dc.subject.other Computational model en
dc.subject.other Data sets en
dc.subject.other Drug effects en
dc.subject.other High-throughput en
dc.subject.other Integer linear programming formulation en
dc.subject.other Ligands and inhibitors en
dc.subject.other Literature search en
dc.subject.other Liver cancer cells en
dc.subject.other Mammalian cells en
dc.subject.other Pharmaceutical industry en
dc.subject.other Signaling pathways en
dc.subject.other Signaling proteins en
dc.subject.other Signalling network en
dc.subject.other Text mining en
dc.subject.other Data mining en
dc.subject.other Gene expression en
dc.subject.other Integer programming en
dc.subject.other Mammals en
dc.subject.other Phosphorylation en
dc.subject.other Pigments en
dc.subject.other Signaling en
dc.title Construction of signaling pathways and identification of drug effects on the liver cancer cell HepG2 en
heal.type conferenceItem en
heal.identifier.primary 10.1109/IEMBS.2010.5626246 en
heal.identifier.secondary http://dx.doi.org/10.1109/IEMBS.2010.5626246 en
heal.identifier.secondary 5626246 en
heal.publicationDate 2010 en
heal.abstract Construction of signaling pathway maps and identification of drug effects are major challenge for pharmaceutical industries. Signaling maps are usually obtained from manual literature search, automated text mining algorithms, or canonical pathway databases (i.e. Reactome, KEGG, STKE, Pathway Studio, Ingenuity etc.) and in some cases they are used in combination with gene expression or mass spec data in an effort to create pathways specific to cell types or diseases. Our approach combines computational models with novel multicombinatorial high-throughput phosphoproteomic data for the functional analysis of signalling networks in mammalian cells. On the experimental front, we subject the cells with hundreds of co-treatment with a diverse set of ligands and inhibitors and we measure phosphorylation events on key signaling proteins using the xMAP technology. On the computational front, we create pathway maps that are cell type specific by fitting our phosphoprotein dataset into generic signaling maps via an Integer Linear programming formulation. To identify drug effects, we monitor the differences of topologies created with and without the presence of drug. In the present work, we use this approach to identify the effects of Nilotinib, a well known anti-cancer drug. © 2010 IEEE. en
heal.journalName 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 en
dc.identifier.doi 10.1109/IEMBS.2010.5626246 en
dc.identifier.volume 2010 en
dc.identifier.spage 6717 en
dc.identifier.epage 6720 en

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