Professor of Mathematics University of Southern California
Los Angeles, California
Seeking to understand the mechanisms driving cancer progression and metastasis.
A multidisciplinary approach is employed to create a model of tumor growth and invasion to identify druggable targets to prevent metastasis.
This innovative and collaborative effort will provide new modeling tools that will advance research in metastatic breast cancer.
In order for metastasis to occur, tumor cells must be able to leave an actively growing tumor, shut off growth processes while they travel to a distant organ and then switch those growth pathways back on at the new location.
In this project co-funded by BCRF and the Jayne Koskinas Ted Giovanis Foundation for Health Policy, Dr. Newton and colleagues will apply methods of cellular biology, chemical engineering, and mathematical and computational modeling to elucidate the molecular drivers of these transitions in a systematic fashion.
They will create a model system of tumor growth and invasion based on analyses of tumor growth, tumor cell behavior and results from microenvironmental and genetic experiments. This 3D model can then be used to test the effects of selected stimuli on tumor cell behavior that will elucidate points in the metastatic process where interventions are possible.
These studies will accelerate discovery of potential targets to prevent metastasis.
Professor Newton received his B.S. in Applied Math/Physics at Harvard University and Ph.D. in Applied Mathematics from Brown University. After a post-doctoral fellowship at Stanford University, he was Assistant and Associate Professor of Mathematics and The Center for Complex Systems Research at the University of Illinois Champaign-Urbana. He is currently Professor of Applied Math, Engineering, and Medicine in the Viterbi School of Engineering and the Norris Comprehensive Cancer Center at the University of Southern California. His research focuses on developing mathematical and computational models of cancer metastasis and progression for breast, lung, and prostate cancers based on circulating tumor cell and longitudinal data sets. He also uses evolutionary game-theoretic models to build stochastic models of tumor growth, dissemination, low-dose chemotherapeutic scheduling and adaptive therapy design. He has held visiting appointments at Brown University, Caltech, The Scripps Research Institute, Hokkaido University, and the KITP at UC Santa Barbara.