题 目: Learning biological dynamics from incomplete measurements
报告人: Shou-Wen Wang, Ph.D.
Postdoc Researcher, Damon Runyon Fellow, Harvard Medical School
时 间: 2月23日（周三）上午9:30-10:30
地 点: ZOOM线上报告
Meeting ID: 746 492 6744
主持人: 林一瀚 研究员
It is increasingly appreciated that components of biological systems are highly interactive over a broad range of time and length scales, and an integrative understanding of the resulting dynamics is necessary. The complexity of most biological systems still far exceeds our measurement capacity, despite numerous innovations in recent decades. Given partial observations of a system, it is an important challenge to infer the underlying dynamics at the system level and make testable predictions. I will first present a physics approach to this challenge where I derived theoretical insights from biophysical models under limited observations (Ph.D. work). Then, given sparse and incomplete high-throughput measurements (single-cell RNA-seq + Lineage tracing), I will integrate theoretical modeling and algorithm development to infer cell differentiation dynamics (postdoc work). My postdoc work has broad implications for sparse measurements in single-cell genomics, and opens up several new problems that I will pursue in my initial faculty years.
Shou-wen Wang obtained both his B.S. and Ph.D. in Physics from Tsinghua University, and joined Dr. Allon Klein’s lab at Harvard Medical School for his postdoc in 2018, working on developing computational methods to infer cell fate choices from single-cell multi-omics data. He is one of 9 inaugural recipients of the Quantitative Biology Award from the Damon Runyon Cancer Research Foundation in the United States.