What should be modeled in cancer: milestones for physical models

Abstract

The field of mathematical oncology is exploding, with increasing efforts directed at laying down the foundation for the phenomena of cancer development, growth, metastases, and response to therapies. This work involves working at multiple spatio-temporal scales. The relative paucity of dynamical data renders many parametrical models challenging and limits their robustness. Concepts from engineering and nonlinear optical systems can be applied to cell signaling in the cancer cell yielding useful models. In this work, we explore the concepts of modularity, retroactivity, and pathway integration and delineate some of the outstanding questions that we believe should be prioritized for modeling in cancer.