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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.