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

Alexopoulos, LG; Melas, IN; Chairakaki, AD; Saez-Rodriguez, J; Mitsos, A

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	https://dspace.lib.ntua.gr/xmlui/handle/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