We developed a logical model that considers major pathways responsible for prostate cancer development. This model has been extensively tested in silico by studying all single and double mutants and its robustness using our pipeline of tools. Additionally, we tailored our logical model to TCGA prostate cancer patients’ and GDSC cell lines’ data to capture their diversity of functioning and response to perturbations. Finally, we simulated the effects of different drugs in prostate-specific cell lines models under different growth conditions in order to find proper combinations of drug concentrations in these cell lines. Present results facilitate the use of logical models in personalized medicine, by allowing the instantiation of patient-specific models, and facilitates the study of patient-specific drug treatments that depend on the specific patient’s response.