AI at the Bench: PKS Engineering Case Studies

​Polyketide synthases (PKS) are large assembly-line enzymes that manufacture a variety of chemicals essential for American biotechnological manufacturing, including fuels, medicines, and bulk chemicals. The engineering of PKS systems is complex, however, owing to the large size and complexity of the enzymes.

Engineering of these proteins requires repeated cycles of designing experiments, building genetic constructs, testing performance, and interpreting results. Artificial intelligence can support several stages of this design–build–test–learn, or DBTL, cycle when its outputs are converted into testable scientific hypotheses.

This session features two student-led presentations describing practical AI applications in PKS engineering. The speakers will demonstrate how AI can assist with hypothesis generation, experimental interpretation, identification of promising engineering strategies, and selection of subsequent experiments. Together, the talks introduce the concept of “AI-in-the-DBTL-cycle,” placing AI within the scientific process rather than above it. AI serves as a scientific sounding board, helping undergraduates anticipate experimental outcomes and interpret their results more quickly.

Attendees will gain a practical framework for using AI while verifying its outputs and maintaining human responsibility for scientific decisions. The session is intended for students, educators, researchers, and others interested in biotechnology, artificial intelligence, or scientific innovation. No prior knowledge of PKS engineering is required. A brief audience discussion and Q&A will follow.