Location
MC 5501
Speaker
Farshad Moradikashkooli, Department of Applied Mathematics, University of Waterloo
Title
Computational Oncology for Precision Medicine: Toward Patient-Specific Digital Twins
Abstract
Computational oncology seeks to integrate mathematical modeling, imaging, and data to improve the understanding and prediction of cancer progression and treatment response. A central challenge lies in describing transport processes that span multiple spatial, temporal, and biological scales, from molecular drug release and cellular uptake to tissue-level perfusion and tumor-scale dynamics. This talk presents a multiphysics, multiscale modeling framework based on coupled systems of differential equations to characterize transport across space, time, and scale in oncology. Illustrative case studies include advanced modeling problems in the tumor microenvironment, chemotherapy and nanoparticle drug transport and delivery, as well as radiopharmaceutical dynamics, with an emphasis on image-based model construction and parameterization. By linking local transport mechanisms to system-level behavior and incorporating patient-specific information derived from medical imaging, these models provide a pathway toward patient-specific digital twins for precision oncology.