Comparing signaling networks between normal and transformed hepatocytes using discrete logical models

Saez-Rodriguez, J; Alexopoulos, LG; Zhang, MS; Morris, MK; Lauffenburger, DA; Sorger, PK

dc.contributor.author	Saez-Rodriguez, J	en
dc.contributor.author	Alexopoulos, LG	en
dc.contributor.author	Zhang, MS	en
dc.contributor.author	Morris, MK	en
dc.contributor.author	Lauffenburger, DA	en
dc.contributor.author	Sorger, PK	en
dc.date.accessioned	2014-03-01T02:01:53Z
dc.date.available	2014-03-01T02:01:53Z
dc.date.issued	2011	en
dc.identifier.issn	00085472	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29262
dc.subject.other	4 (2 aminoethylamino) 1,8 dimethylimidazo[1,2 a]quinoxaline	en
dc.subject.other	epidermal growth factor	en
dc.subject.other	epidermal growth factor receptor	en
dc.subject.other	glycogen synthase kinase 3	en
dc.subject.other	heat shock protein 27	en
dc.subject.other	I kappa B kinase inhibitor	en
dc.subject.other	immunoglobulin enhancer binding protein	en
dc.subject.other	insulin	en
dc.subject.other	insulin receptor substrate	en
dc.subject.other	interleukin 1alpha	en
dc.subject.other	interleukin 6	en
dc.subject.other	Janus kinase	en
dc.subject.other	mammalian target of rapamycin	en
dc.subject.other	mitogen activated protein kinase	en
dc.subject.other	phosphatidylinositol 3 kinase inhibitor	en
dc.subject.other	protein kinase B	en
dc.subject.other	protein p85	en
dc.subject.other	Ras protein	en
dc.subject.other	STAT protein	en
dc.subject.other	transforming growth factor alpha	en
dc.subject.other	tumor necrosis factor alpha	en
dc.subject.other	article	en
dc.subject.other	cancer cell culture	en
dc.subject.other	cell transformation	en
dc.subject.other	controlled study	en
dc.subject.other	down regulation	en
dc.subject.other	enzyme activation	en
dc.subject.other	enzyme activity	en
dc.subject.other	human	en
dc.subject.other	human cell	en
dc.subject.other	information science	en
dc.subject.other	liver cell	en
dc.subject.other	mathematical model	en
dc.subject.other	prior knowledge network	en
dc.subject.other	priority journal	en
dc.subject.other	protein degradation	en
dc.subject.other	protein localization	en
dc.subject.other	protein phosphorylation	en
dc.subject.other	protein protein interaction	en
dc.subject.other	signal transduction	en
dc.subject.other	statistical analysis	en
dc.subject.other	upregulation	en
dc.subject.other	Cell Line, Transformed	en
dc.subject.other	Cell Line, Tumor	en
dc.subject.other	Hepatocytes	en
dc.subject.other	Humans	en
dc.subject.other	Models, Biological	en
dc.subject.other	Signal Transduction	en
dc.title	Comparing signaling networks between normal and transformed hepatocytes using discrete logical models	en
heal.type	journalArticle	en
heal.identifier.primary	10.1158/0008-5472.CAN-10-4453	en
heal.identifier.secondary	http://dx.doi.org/10.1158/0008-5472.CAN-10-4453	en
heal.publicationDate	2011	en
heal.abstract	Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of ""omic"" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets. ©2011 AACR.	en
heal.journalName	Cancer Research	en
dc.identifier.doi	10.1158/0008-5472.CAN-10-4453	en
dc.identifier.volume	71	en
dc.identifier.issue	16	en
dc.identifier.spage	5400	en
dc.identifier.epage	5411	en