Content
Friday, March 6
4:00 p.m.
Avery Hall 115
A reception will precede the lecture in room 348 of Avery Hall at 3pm. All faculty, staff, and students, and lecture attendees are welcome to attend.
Speaker
Qing Nie
Institution: University of California, Irvine
Title
Systems Learning of Single Cells
Abstract
Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from complex, multiscale interactions among genes and cells across space and time. Although single-cell omics technologies provide unprecedented resolution for profiling cellular heterogeneity, they typically require fixation of cells, resulting in the loss of temporal, spatial, and intercellular interaction information. This raises fundamental questions: How can we reconstruct temporal dynamics from single or multiple snapshots of single-cell omics data? How can we infer interactions among cells—such as cell–cell communication—from static gene expression measurements? In this talk, I will present a suite of computational methods we have recently developed to learn single-cell omics data as a spatiotemporal and interactive system. These methods integrate systems biology modeling, dynamical systems theory, machine learning, and optimal transport. I will demonst! rate thei r application to a range of complex biological systems in development, regeneration, and disease, highlighting their ability to uncover dynamic processes and intercellular communication. Finally, I will discuss open methodological challenges in systems-level learning from single-cell data.