AI for Synthetic Biology

Synthetic biology aims to design novel or improved biological systems using engineering principles, with broad applications in medicine, chemicals, food, and agriculture. Because biological systems are complex, synthetic biology remains difficult to perform in a quantitative and predictive manner.

This talk highlights recent work on AI tools and AI-powered self-driving biofoundries that accelerate the design-build-test-learn cycle. Examples include ECNet for protein engineering, CLEAN for enzyme function prediction, EZSpecificity for enzyme substrate specificity prediction, generative AI for mitochondrial targeting sequences, and BioAutomata for protein, pathway, and metabolic engineering.

Dr. Huimin Zhao is the Steven L. Miller Chair of chemical and biomolecular engineering at UIUC, 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.