The overall goal of my research is to develop and validate a mathematical model that predicts how the spatiotemporal distribution of some critical nutrients (oxygen, glucose, and lactate) could influence metabolism and therefore tumor development. We divide the tumor cells into different subpopulations according to the nutrient level in their microenvironment; in particular, we hypothesize that the tumor cells, in response to the availability of local nutrients, would go through different metabolism to adapt to the environment and result in different tumor development. Therefore, we are working to develop a computational-experimental approach to model the growth of different subpopulations and conversion among them, as well as nutrient variance due to consumption, production and diffusion. We will use multi-scale imaging methods (including real time optical imaging and diffusion magnetic resonance imaging) to help validate and apply the mathematical model. We hypothesize that given the early time point data on the distribution of nutrient level, we will be able to predict the spatiotemporal change in a tumor’s metabolic pathway, thereby forecasting future tumor growth and response to therapy. Our current work is focusing on development of the mathematical model and establishment of cell lines with specific nanosensor to detect glucose and lactate.