We leverage multi-region, longitudinal, and/or single-cell genomic data combined with agent-based computational model to reveal the quantitative principles for the evolutionary dynamics of tumor growth and metastasis.
We integrate multi-region sequencing, single cell transcriptomics and computational models to understand how cancer cells evolve to escape our immune surveillance and how immune selection drive the evolutionary trajectories of cancer cells.
We combine lineage tracing, single-cell transcriptomics and mathematical models to study the clonal dynamics of stem cells during tissue development and renewal.
We develop computational methods for analyzing simultaneous single-cell transcriptomic and lineage tracing data, with an aim to understand the cell-state transitions in organ development and tumor evolution.