Speaker: Dr. Qing Nie is a University of California Presidential Chair and a Distinguished Professor of Mathematics and Developmental & Cell Biology at University of California, Irvine. Dr. Nie is the director of the NSF-Simons Center for Multiscale Cell Fate Research jointly funded by NSF and the Simons Foundation. In research, Dr. Nie uses systems biology and data-driven methods to study complex biological systems with focuses on single-cell analysis, multiscale modeling, cellular plasticity, stem cells, embryonic development, and their applications to diseases. Dr. Nie has published more than 250 research articles, including more than 50 papers in journals such as Nature, Science, Nature Methods, PNAS, Nature Machine Intelligence, Cancer Cells, Nature Communications. In training, Dr. Nie has supervised more than 60 postdoctoral fellows and PhD students. In 2025, Dr. Nie was ranked #1 by ScholarGPS based on citation metrics as Highly Ranked Scholar in two areas: a) Single-cell transcriptomics & b) Transcriptomics technologies for Prior Five Years. Dr. Nie is a fellow of the American Association for the Advancement of Science (AAAS), American Physical Society (APS), Society for Industrial and Applied Mathematics (SIAM), and American Mathematical Society (AMS).

Date: July 14, 2025

Time: 10:30-12:00 am

Location: B936, Zhixin Building, Shandong University

Sponsor: School of Mathematics, Shandong University

Abstract:

Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from multiscale interplays among cells and genes in space and time. While single-cell omics data provides an unprecedented opportunity to profile cellular heterogeneity, the technology requires fixing the cells, often leading to a loss of spatiotemporal and cell interaction information. How to reconstruct temporal dynamics from single or multiple snapshots of single-cell omics data? How to recover interactions among cells, for example, cell-cell communication from single-cell gene expression data? I will present a suite of our recently developed computational methods that learn the single-cell omics data as a spatiotemporal and interactive system. Those methods are built on a strong interplay among systems biology modeling, dynamical systems approaches, machine-learning methods, and optimal transport techniques. The tools are applied to various complex biological systems in development, regeneration, and diseases to show their discovery power. Finally, I will discuss the methodology challenges in systems learning of single-cell data.

For more information, please visit:

https://www.view.sdu.edu.cn/info/1020/204425.htm