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SKLBE学术论坛

SKLBE学术论坛

第495次SKLBE学术论坛(美国伊利诺伊大学厄巴纳-香槟分校 Huimin Zhao 教授)

作者: 发布时间:2019-11-18 17:11:00

以下为第495SKLBE学术论坛信息,请阅。



题目:Biosystems Design by Directed Evolution

报告人:美国伊利诺伊大学厄巴纳-香槟分校 Huimin Zhao 教授

时间:   2019-11-20(周三)14:00-15:00

地点:   实验18315

主持人:  张立新教授

Dr. Huimin Zhao is the Steven L. Miller Chair ofchemical and biomolecular engineering, and professor of chemistry,biochemistry, biophysics, and bioengineering at the University of Illinois atUrbana-Champaign (UIUC).  He received hisB.S. degree in Biology from the University of Science and Technology of Chinain 1992 and his Ph.D. degree in Chemistry from the California Institute ofTechnology in 1998 under the guidance of Nobel Laureate Dr. FrancesArnold.  Prior to joining UIUC in 2000,he was a project leader at the Industrial Biotechnology Laboratory of the DowChemical Company.  He was promoted tofull professor in 2008.  Dr. Zhao has authoredand co-authored over 310 research articles and over 25 issued and pendingpatent applications with several being licensed by industry.  In addition, he has given plenary, keynote orinvited lectures in over 380 international meetings, universities, industries,and research institutes.  Twenty-six (26)of his former graduate students and postdocs became professors or principalinvestigators in the United States (10), China (Mainland 10, Taiwan 1), Korea(2), Singapore (2), and Egypt (1). 

Biosystems Design by Directed Evolution


By mimicking the Darwinian evolution in the test tube, directed evolution has become a powerful and indispensable tool for the design and engineering of biological systems including proteins, pathways, and genomes for fundamental research and biotechnological applications. In this talk, I will give a historical account of the directed evolution field and discuss the challenges and opportunities in directed evolution. I will highlight our recent work on the development and application of novel directed evolution tools for pathway engineering and genome engineering. In addition, I will introduce a machine learning enabled directed evolution approach for biosystems design.