A pathophysiology driven spatial dynamic modeling framework for personalized prediction and precision medicine

Summary Predictive personalized healthcare and precision medicine enable a new era of medicine, in which traditional physiological information and new clinical data including genetic data, imaging data, and healthcare data come together to match the right patient with the right treatment at the right time. With healthcare and clinical technology rapidly grow, the vast and varied amounts of clinical data become widely accessible and usable. However, current modeling techniques lack of combining pathophysiology with imaging data. Therefore, developing an innovative data analytical tool that combines personalized clinical data with the existing sciences of epidemiology and clinical medicine becomes an urgent need in this new area. The overall vision of this research project is to develop a pathophysiology driven spatial dynamic modeling (PDSDM) approach for personalized healthcare prediction and precision medicine. Our five-year goals are to develop the PDSDM computational modeling platform and validate this platform on patient data for various diseases. In specific, we will develop an interactive computational platform to build the PDSDM model and develop a computational module to simulate the model automatically. Then we will develop a model calibration module by employing the clinical patient data to parameterize mathematical models arising from physiological signaling pathway networks and will also incorporate the imaging data as the spatial computational domain; moreover, optimal personalized treatment studies will be performed on this computational modeling platform for current available clinical trials. This innovative framework will integrate mathematical modeling, computational methods, data analysis, and data-driven optimization techniques to provide a personalized spatial computational model for each individual. We will validate this new framework on various biomedical diseases such as cardiovascular disease, chronic pancreatitis, and Alzheimer’s disease with existing clinical and biological data. The proposed research is significant because it will provide the 3D prediction for personalized disease progression which would evaluate personalized disease risk more accurately. It will also provide a systematic way to assess the available treatment plans virtually then to provide an optimal treatment suggestion for each individual.