报告人： Prof. Huimin Zhao

University of Illinois at Urbana-Champaign

报告时间：10月16日（周一）15:00-16:15

报告地点：闵行校区生命药学楼树华多功能厅

个人简介：

Dr. Huimin Zhao is the Steven L. Miller Chair of chemical and biomolecular engineering at the University of Illinois at Urbana-Champaign (UIUC), director of NSF AI Institute for Molecule Synthesis (moleculemaker.org), and Editor in Chief of ACS Synthetic Biology. He received his B.S. degree in Biology from the University of Science and Technology of China in 1992 and his Ph.D. degree in Chemistry from the California Institute of Technology in 1998 under the guidance of Nobel Laureate Dr. Frances Arnold.  Prior to joining UIUC in 2000, he was a project leader at the Industrial Biotechnology Laboratory of the Dow Chemical Company. He was promoted to full professor in 2008. Dr. Zhao has authored and co-authored over 420 research articles and over 30 issued and pending patent applications. In addition, he has given over 470 plenary, keynote, or invited lectures. Thirty-six (36) of his former graduate students and postdocs became professors or principal investigators around the world. Dr. Zhao received numerous research and teaching awards and honors such as AIChE FP&B Division Award, ECI Enzyme Engineering Award, ACS Marvin Johnson Award, and SIMB Charles Thom Award. His primary research interests are in the development and applications of synthetic biology, machine learning, and laboratory automation tools to address society’s most daunting challenges in health, energy, and sustainability.

报告摘要：

Synthetic biology aims to design novel or improved biological systems using engineering principles, which has broad applications in medical, chemical, food, and agricultural industries. Thanks to the rapid advances in DNA sequencing and synthesis, genome editing, artificial intelligence/machine learning (AI/ML), and laboratory automation in the past two decades, synthetic biology has entered a new phase of exponential growth. In this talk, I will highlight our recent work on the development of a self-driving biofoundry and AI/ML tools for synthetic biology applications. Examples include but are not limited to: (1) BioAutomata: a self-driving biofoundry for pathway engineering and protein engineering, (2) ECNet: an AI tool for enzyme engineering, (3) CLEAN: an AI tool for enzyme function prediction, and (4) FAST-RiPP & FAST-NPS: an automated and scalable platform for rapid discovery of bioactive natural products.