Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data

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

dc.contributor.author	Mitsos, A	en
dc.contributor.author	Melas, IN	en
dc.contributor.author	Siminelakis, P	en
dc.contributor.author	Chairakaki, AD	en
dc.contributor.author	Saez-Rodriguez, J	en
dc.contributor.author	Alexopoulos, LG	en
dc.date.accessioned	2014-03-01T01:30:52Z
dc.date.available	2014-03-01T01:30:52Z
dc.date.issued	2009	en
dc.identifier.issn	1553-734X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19659
dc.subject	Drug Effects	en
dc.subject	Integer Linear Program	en
dc.subject.classification	Biochemical Research Methods	en
dc.subject.classification	Mathematical & Computational Biology	en
dc.subject.other	gefitinib	en
dc.subject.other	interleukin 1alpha	en
dc.subject.other	phosphoprotein	en
dc.subject.other	antineoplastic agent	en
dc.subject.other	article	en
dc.subject.other	cell strain HepG2	en
dc.subject.other	drug effect	en
dc.subject.other	drug efficacy	en
dc.subject.other	human	en
dc.subject.other	human cell	en
dc.subject.other	algorithm	en
dc.subject.other	biological model	en
dc.subject.other	metabolism	en
dc.subject.other	methodology	en
dc.subject.other	pharmacology	en
dc.subject.other	protein database	en
dc.subject.other	proteomics	en
dc.subject.other	reproducibility	en
dc.subject.other	signal transduction	en
dc.subject.other	Algorithms	en
dc.subject.other	Antineoplastic Agents	en
dc.subject.other	Databases, Protein	en
dc.subject.other	Hep G2 Cells	en
dc.subject.other	Humans	en
dc.subject.other	Models, Biological	en
dc.subject.other	Pharmacology	en
dc.subject.other	Phosphoproteins	en
dc.subject.other	Proteomics	en
dc.subject.other	Reproducibility of Results	en
dc.subject.other	Signal Transduction	en
dc.title	Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data	en
heal.type	journalArticle	en
heal.identifier.primary	10.1371/journal.pcbi.1000591	en
heal.identifier.secondary	http://dx.doi.org/10.1371/journal.pcbi.1000591	en
heal.identifier.secondary	e1000591	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Understanding the mechanisms of cell function and drug action is a major endeavor in the pharmaceutical industry. Drug effects are governed by the intrinsic properties of the drug (i.e., selectivity and potency) and the specific signaling transduction network of the host (i.e., normal vs. diseased cells). Here, we describe an unbiased, phosphoproteomic-based approach to identify drug effects by monitoring drug-induced topology alterations. With our proposed method, drug effects are investigated under diverse stimulations of the signaling network. Starting with a generic pathway made of logical gates, we build a cell-type specific map by constraining it to fit 13 key phopshoprotein signals under 55 experimental conditions. Fitting is performed via an Integer Linear Program (ILP) formulation and solution by standard ILP solvers; a procedure that drastically outperforms previous fitting schemes. Then, knowing the cell's topology, we monitor the same key phosphoprotein signals under the presence of drug and we re-optimize the specific map to reveal drug-induced topology alterations. To prove our case, we make a topology for the hepatocytic cell-line HepG2 and we evaluate the effects of 4 drugs: 3 selective inhibitors for the Epidermal Growth Factor Receptor (EGFR) and a non-selective drug. We confirm effects easily predictable from the drugs' main target (i.e., EGFR inhibitors blocks the EGFR pathway) but we also uncover unanticipated effects due to either drug promiscuity or the cell's specific topology. An interesting finding is that the selective EGFR inhibitor Gefitinib inhibits signaling downstream the Interleukin-1alpha (IL1a) pathway; an effect that cannot be extracted from binding affinity-based approaches. Our method represents an unbiased approach to identify drug effects on small to medium size pathways which is scalable to larger topologies with any type of signaling interventions (small molecules, RNAi, etc). The method can reveal drug effects on pathways, the cornerstone for identifying mechanisms of drug's efficacy. © 2009 Mitsos et al.	en
heal.publisher	PUBLIC LIBRARY SCIENCE	en
heal.journalName	PLoS Computational Biology	en
dc.identifier.doi	10.1371/journal.pcbi.1000591	en
dc.identifier.isi	ISI:000274229000009	en
dc.identifier.volume	5	en
dc.identifier.issue	12	en