Cancer systems biology

Cancer is a complex and deadly disease. Staggering mortality and recurrence rates reflect the robust nature of the cancer cell for survival. These survival mechanisms manifest in the form of drug resistance, which has proven to be a major obstacle for emerging targeted therapies. Massive experimental efforts have been fueled by the advances and availability of high-throughput technologies. However, gained knowledge is not translating into treatment. Consequently, experts and funding organizations are calling for a paradigmshift in the field of Cancer Biology,which moves the focus from single molecular targets to pathways and systems. Approaches in Systems Biology will be critical for this endeavor. Mathematical and Statistical models are central to Systems Biology approaches, and hold tremendous promise for advancing our understanding about the disease. In this Chapter, we describe several modeling paradigms and their applications to Cancer Biology. The methods described fall into two complementary categories: deterministic models and statistical graphical models. Strengths of these approaches include: flexibility of the modeling paradigms to generalize to different data and applications, and the ability of the model to make predictions about quantities, which may be difficult or impossible to measure in vivo. Current challenges regarding inference and computation are detailed. A rich spectrum of applications are provided, including, predicting system-wide effects of drug treatment, disease prognosis, tumor classification, forecasting treatment outcomes, and predicting survival for individual patients.