Professor, Department of Mathematics, Department of Biomedical Engineering, and Department of Development and Cell Biology Director, Center for Mathematical and Computational Biology University of California
Jayne Koskinas Ted Giovanis Foundation for Health and Policy Partnership
Seeking to understand the cellular processes leading to drug restistance.
A cross-disciplinary approach is applied to identify new targets to prevent resistance to anti-cancer therapies.
This collaborative study will accelerate discoveries that will inform strategies to improve patient outcomes.
Drug resistance is a key impediment to breast cancer therapy. While genetic mechanisms of drug resistance have been a focus of many studies, the ability of a cell to dynamically evade drugs through non-genetic means is an often-overlooked mechanism for why drugs may not be effective.
Cellular plasticity–the ability to interconvert between different functional states–gives rise to dynamic tumor heterogeneity through the generation of biologically and genetically distinct tumor cell subpopulations with diverse properties, including susceptibility to therapy.
Drs. Nie and Heiser believe that cell plasticity contributes to drug resistance. In this project co-funded by BCRF and Jayne Koskinas Ted Giovanis Foundation for Health and Policy, they will employ an integrated experimental and modeling approach that can be used to identify rational approaches to overcome plasticity-induced resistance.
The goal of this collaborative project is to identify non-genetic mechanisms of drug resistance that can inform more effective treatments for patients with breast cancer.
Originally trained in scientific computing and mechanics during his PhD study at The Ohio State University and as a postdoctoral fellow at the University of Chicago working on fluids and materials, during the past 15 years Dr. Nie has devoted his research effort to systems biology of morphogenesis, regulatory networks, cell signaling, and stem cells with emphasis on addressing challenging and complex biological questions in close collaboration with experimentalists. One of his major research focuses is to develop data-driven predictive and multiscale models and powerful computational tools that target specific and important questions in biology and medicine.