The structure and functioning of signaling networks is complex, and they are differently deregulated in different contexts in non-trivial ways. To ensure efficiency of the drug treatments, a good knowledge of these complex interactions and how patient mutations affect the cellular fate is necessary. Among modeling techniques, logic modeling has proven to be very versatile and able to provide useful biological insights. Here, we show how to build a logic model from literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology, using a prostate cancer example that involves some of the key phosphorylation pathways of this malignancy. We use data describing the phosphorylation response of key proteins in prostate cancer cell lines in response to the addition of several ligands and inhibitors (Lescarbeau & Kaplan 2014).