The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims 
to create a network-based understanding of biology by cataloging changes in gene 
expression and signal transduction that occur when cells are exposed to a 
variety of perturbations. It is helpful for understanding cell pathways and 
facilitating drug discovery. Here, we developed a novel approach to infer 
cell-specific pathways and identify a compound's effects using gene expression 
and phosphoproteomics data under treatments with different compounds. Gene 
expression data were employed to infer potential targets of compounds and create 
a generic pathway map. Binary linear programming (BLP) was then developed to 
optimize the generic pathway topology based on the mid-stage signaling response 
of phosphorylation. To demonstrate effectiveness of this approach, we built a 
generic pathway map for the MCF7 breast cancer cell line and inferred the 
cell-specific pathways by BLP. The first group of 11 compounds was utilized to 
optimize the generic pathways, and then 4 compounds were used to identify 
effects based on the inferred cell-specific pathways. Cross-validation indicated 
that the cell-specific pathways reliably predicted a compound's effects. 
Finally, we applied BLP to re-optimize the cell-specific pathways to predict the 
effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) 
according to compound-induced topological alterations. Trichostatin A and MS-275 
(both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell 
growth arrest via activation of p53 and p21; the effects of digoxigenin were 
totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also 
promoted cell growth via activated HDAC1 and its downstream pathway. Our 
approach was also applied to the PC3 prostate cancer cell line, and the 
cross-validation analysis showed very good accuracy in predicting effects of 4 
compounds. In summary, our computational model can be used to elucidate 
potential mechanisms of a compound's efficacy.