UBC Math-Bio Seminar: Yu-Chen Cheng

Understanding how cells change over time is fundamental to studying development, differentiation, and regeneration. Recent advances in single-cell genomics provide detailed measurements of cellular states, but these measurements are typically limited to discrete snapshots, leaving the underlying dynamics largely hidden. In this talk, I will introduce our recent work using optimal transport and gradient-flow-based approaches to reconstruct continuous cellular trajectories from time-series single-cell data. I will highlight two frameworks developed in our recent studies (PNAS, 2024; Cell Systems, 2026) and discuss how mathematical modelling can help reveal cellular dynamics from sparse observations. I will conclude with future research directions integrating population dynamics, spatial and multimodal measurements, and regulatory network inference to better understand how cellular systems evolve.