Associate Professor, Biomedical Engineering, Biomedical Engineering
- PhD, Chemical Engineering, University of Michigan
- Postdoc, Systems Biology, Harvard Medical School
Biochemistry, Biomedical Engineering, Biotechnology, Cancer Biology, Cell and Developmental Biology, Computational Biology, Epigenetics, Experimental Pathology, Metabolism, Molecular Biology, Molecular Pharmacology, Molecular Physiology and Biological Physics, Translational Science
Cancer systems biology, Single-cell quantitative biology, Computational modeling
Our research in the field of Systems Biology is focused on understanding the fundamental mechanisms of human cell responses to environmental and therapeutic perturbations. These responses vary among distinct cell types, or even among populations of genetically identical cells exposed to uniform conditions. Such cell-to-cell heterogeneities can profoundly impact cellular response to many therapeutic treatments such as cancer targeted- and immune-therapies. For example, they complicate cancer therapies by giving rise to subpopulations of therapy-resistant tumor cells that ultimately lead to disease progression in cancer patients. Predicting and understanding the mechanisms that underpin cellular plasticity and heterogeneous cell fate decisions is a key challenge for quantitative biology and precision medicine. The challenge is significant because biomolecules in cells do not act in isolation but are embedded in multi-component networks that are subject to homeostatic control. Uncovering the rules that govern such seemingly complex networks and how they vary from one cell to the next would be difficult, if not impossible, using conventional methods. In our laboratory, we develop and use a combination of experimental, analytical, and computational toolkits to assay these sorts of networks and interrogate their complexities.
What makes our inter-disciplinary approach unique is its focus on: (1) the innovative deployment of cutting-edge, high-throughput, multiplexed technologies to generate hypothesis-driven datasets, (2) the application of modern computational tools to analyze such high-dimensional datasets, and (3) the creation of quantitative models of cellular responses that are predictive at single-cell, molecular and network levels. The iterative use of these methods has enabled us to discover novel biological mechanisms, experimentally validate these mechanisms, and utilize them to guide the development of better therapies for precision medicine.