AI for Synthetic Biology

Abstract

Synthetic biology aims to design novel or improved biological systems using engineering principles, which has broad applications in medical, chemical, food, and agricultural industries. However, due to the complexity of biological systems, performing synthetic biology in a quantitative and predictive manner remains a challenge. In recent years, thanks to advances in data science, artificial intelligence (AI) that allows computers to learn from experience has emerged as a potentially powerful tool to address this challenge. In this talk, I will highlight our recent work on the development of AI tools and an AI-powered self-driving biofoundry to accelerate the design-build-test-learn cycle in synthetic biology. Examples include but are not limited to: (a) ECNet: a deep learning model for protein engineering (Luo et al. Nature Communications 2021); (b) CLEAN: an AI tool for enzyme function prediction (Yu et al. Science 2023); (c) EZSpecificity: an AI tool for enzyme substrate specificity prediction (Cui et al. Nature 2025); (d) design of novel mitochondrial targeting sequences using generative AI (Boob et al. Nature Communications 2025), and (e) BioAutomata: an AI-powered self-driving biofoundry for protein engineering, pathway engineering, and metabolic engineering (Hamedi et al. Nature Communications 2019; Singh et al. Nature Communications 2025).

Biography

Steven L. Miller Chair of chemical and biomolecular engineering at the University of Illinois Urbana-Champaign (UIUC) Huimin Zhao is also the director of NSF AI Institute for Molecule Synthesis, NSF iBioFoundry, and NSF Global Center for Biofoundry Applications, and Editor in Chief of ACS Synthetic Biology. He received his BS degree in biology from the University of Science and Technology of China in 1992 and his PhD degree in chemistry from the California Institute of Technology under the guidance of Nobel Laureate Frances Arnold in 1998. Prior to joining UIUC in 2000, he was a project leader at the Dow Chemical Company. Zhao has authored and co-authored over 480 research articles and over 30 issued and pending patent applications. In addition, he has given over 550 plenary, keynote, or invited lectures. Forty of his former graduate students and postdocs became professors or principal investigators around the world. Zhao received numerous research and teaching awards and honors such as ECI Enzyme Engineering Award and NSF CAREER Award. His primary research interests are in the development and applications of synthetic biology, artificial intelligence, and laboratory automation tools to address society’s most daunting challenges in health, energy, and sustainability.