Today, we have the ability to take a snapshot of the transcriptional activity of genes, identify mutations, and quantify the protein and metabolite content of cells. All of these measurements can be made at a systems-wide level. These data present the potential to greatly improve our ability to characterize and treat disease; however, the rate of data production is far outpacing our ability to analyze, interpret, and ultimately build predictive tools in medicine. My lab takes a complementary dry and wet lab approach to close the gap between raw data and biological understanding. Our dry lab research focuses on developing and implementing computational tools that distill this large pool of genome-scale data into actionable hypothesis. Our wet lab research brings the computational modeling to the bench where we aim to characterize the genomic components that contribute to drug mode of action in cancer biology.