Biology has been transformed by new technologies that provide detailed descriptions of the molecular changes that occur in diseases. However, it is difficult to use these data to reveal new therapeutic insights for several reasons. Despite their power, each of these methods still only captures a small fraction of the cellular response. Moreover, when different assays are applied to the same problem, they provide apparently conflicting answers. I will show that network modeling reveals the underlying consistency of the data by identifying small, functionally coherent pathways linking the disparate observations. We have used these methods to analyze how oncogenic mutations alter signaling and transcription and to prioritize experiments aimed at discovering therapeutic targets.