Research
Research in the lab is built on our multi-disciplinary experience in computational bioengineering, systems biology and quantitative pharmacology, and benefits from active collaboration with leaders in clinical oncology, epigenetics and proteomics research. The lab is currently focused on the following areas of research:
Single-Cell Quantitative Biology – Multi-parametric analysis of cellular response to perturbation
Classical pharmacology has been relying primarily on population-average measurements to address variance between responses of genetically different cell populations to stimuli and therapeutic agents. Clonal populations of cells, however, behave non-identically when exposed to a uniform concentration or dose of a stimulus or drug. Variability with respect to the amplitude and timing of response and the consequential phenotypes is observed among genetically identical cells. Although not obvious from the most frequently studied dose-response metrics (e.g. potency; IC50), cell-to-cell variability can limit maximal effect of a drug both in the short-term (e.g. Emax in cell culture) and in the long-term (e.g. residual disease). We use a range of multiplexed, time-lapse live and fixed single-cell assays and quantitative modeling as a means to identify the origins of heterogeneity in cellular response at a single-cell level, and determine whether or when heterogeneity arises from stochastic or feedback-regulated differences in biochemical processes, differences in cell cycle state, epigenetic state reprogramming, or the pre-existing subpopulations of stem-like cells. In addition to our fundamental understanding of cellular signaling mechanisms, this approach is likely to provide a significant impact on our choice of which drug or drug combinations to explore therapeutically.
Systems Biology of Cancer – Adaptive regulation of tumor cell fate decisions
The discovery of driver oncogenes provides new opportunities for the development of molecularly targeted therapies. However, patient benefit is often temporary and limited by drug adaptation and resistance. Epigenetic switching following oncogene inhibition generates sub-populations of drug-tolerant cells that limit therapeutic efficacy and constitute a reservoir from which stably resistant clones are ultimately selected and drive disease progression. Preventing therapeutic adaptation is likely key to durable therapy; it requires: 1) a system-wide understanding of the molecular networks involved in adaptive drug responses, and 2) single-cell understanding of how they regulate the fate of a tumor cell. We have been utilizing a fusion of experimental and computational techniques to identify molecular drivers of adaptive resistance and phenotype switching in response to targeted therapies, and predict and test efficient ways to block the heterogeneous populations of drug-resistant cells with the ultimate goal of maximizing tumor cell killing.
Computational Biology – Multi-scale modeling of bio-molecular networks and reactions
Advance in biomedical research increasingly depends on emerging technologies to profile complex biological systems. Sophisticated analytical methods are required to dissect the interactions between the measured components and uncover how they determine, at a range of spatial and temporal scales, the function of the system. We seek to build, refine and use data-driven, system-wide models of cellular function that nonetheless contain mechanistic information on the activities of individual bio-molecules. We have built multi-scale computational tools that combine probabilistic and ODE modeling to investigate the role of TNF signaling in immunity to M. tuberculosis or to study regulation of GPCR signaling on the cell membrane. We also use statistical modeling techniques such as partial least squares regression, elastic nets, and information theory to uncover important determinants of regulation from complex high-dimensional data spaces and identify measurements of network activity that are predictive of a specific tumor cell phenotype. These and other mathematical approaches that capture dynamic, multi-factorial differences in signaling networks across tumor cell types or cancer drugs are essential components of our research.
Our research is made possible with funding support from the following organizations:
