This talk is part of the ISP Forum series.

Abstract: High-throughput techniques, especially at the single cell level, have greatly expanded our knowledge of cellular processes. With the increasing availability of data, a fundamental question arises: how can we leverage this data to gain mechanistic insights? Unlike static data typically targeted by statistics-based machine learning approaches, single cell data are snapshots from the dynamical state space of a cell having interacting components that dictate the temporal evolution of the system. Consequently, we witness a growing convergence of two disciplines: data science and systems biology. The latter seeks to unravel qualitative and quantitative causal relationships among cellular components, as well as their functions within the broader context of cell regulatory networks, all through the lens of dynamical systems theory. Consequently, an emerging new direction is to integrate the vast amount available single cell data into systems biology modeling to study complex cellular processes.
During this presentation, I will delve into our endeavors on deducing the comprehensive governing dynamical equations of cells using both snapshot and time series single-cell data. I will first briefly summarize our published work (Sci Adv 2020, Cell 2022, eLife 2022), then focus on a set of new-generation approaches on analyzing single cell genomic data and cell images.

Biography: Dr Xing received B.S. in Chemistry from Peking University, M.S. in Chemical Physics from University of Minnesota, and PhD in Theoretical Chemistry from UC Berkeley. After being a postdoc researcher in theoretical biophysics at UC Berkeley and an independent fellow at Lawrence Livermore National Laboratory, he assumed his first faculty position at Virginia Tech, then moved to University of Pittsburgh in 2015. Currently Dr Xing is a professor in the Computational and Systems Biology Department, School of Medicine, and an affiliated faculty member of Department of Physics and Astronomy, University of Pittsburgh. He is also an affiliated member of University of Pittsburgh Hillman Cancer Center. Dr Xing’s research uses statistical and chemical physics, dynamical systems theory, machine learning, mathematical/computational modeling in combination with quantitative measurements to study the dynamics and mechanics of biological processes.