The goal of my work is the development and application of numerical methods to understand signal transduction cascades in cells and their dysregulation in cancer. My group efforts comprise a tri-partite approach to study cancer biology: (1) Development of modelling and simulation tools necessary to study signalling events across multiple spatiotemporal time-scales. To this end we use techniques of statistical mechanics, molecular simulation, mesoscale modelling, reaction kinetics, and cell-population modelling to develop a systems-level description of cellular environments. (2) Use developed tools to study signalling processes relevant to cancer phenotypes. (3) Collaborate with experimental and theoretical groups to test and expand our hypotheses to develop a fundamental understanding of the rules that govern functional genomics and systems biology. My group currently co-develops and contributes to the PySB (www.pysb.org) modelling platform, which uses a novel modelling-with-programs paradigm to biological signalling pathway simulations. With this approach, the biological knowledge is encoded into executable programs that enhance our capabilities to express, share, and revise our understanding of complex interactions at a large signalling network scale. We have employed a combination of this modelling framework along with in-house developed numerical methods to explore proposed mechanistic hypotheses in the literature about cell death regulation. With our work we were able to elucidate important aspects of signalling, identify gaps in our knowledge, and bring a consensus to the field about extrinsic apoptosis regulation. [Lopez et al. Submitted] My lab is located at the Vanderbilt-Ingram Cancer Center in Vanderbilt University School of Medicine; one of the leading institutions for cancer research in the world and consistently ranked in the top five for NIH funding in the USA. My work provides a theoretical perspective to experimental and clinical efforts in the department and university with the goal of developing a better understanding of cancer development and treatment.