This paper presents a computational model of tumor growth in which the metabolism inside each individual cell directly influences growth of the tumor as a whole. The model predicts dynamic metabolic reprogramming that occurs in cancer cells.
This work reports a novel model of VEGF and TSP1, two potent angiogenic factors, in human cancer patients. With the model, we gain new insight into the impact of tumor receptor heterogeneity on the response to treatment and identify potential tissue biomarkers.
This perspective presents a case for how to answer outstanding questions related to cancer immunotherapy. An integrative approach is needed: applying mechanistic and statistical modeling, establishing consistent and widely adopted experimental tools to generate systems-level data, and creating sustained mechanisms of support.