Research Overview
Cancer Biology: Progression of cancer has been widely viewed as following Darwinian evolution, where cells acquiring somatic driver mutations are positively selected, leading to preferential expansions of clones carrying these mutations. Advances in high-throughput DNA- and RNA-sequencing have substantially increased our understanding of late-stage cancer. Yet we still lack a quantitative understanding of the necessary and sufficient early conditions and steps required for the evolution of normal cells into cancer cells.
We design experimental and computational models of early cancer to (i) capture mutational and transcriptional trajectories over time at single-cell resolution, and (ii) quantify how different driver mutation interactions lead to cooperation or competition amongst cells.Population Biomedicine & Causality: We develop machine learning techniques for applications to large-scale biomedical data, such as scRNA-seq and the UK Biobank. For example, we develop (non-)parametric probabilistic and neural network approaches to (i) extract higher-order interactions from gene expression data, and (ii) quantify causal interactions amongst DNA variants leading to physiological outcomes.