(457b) DIC Wang Award Lecture: Optimization and AI-guided explorations in biochemical engineering

In this talk, we will review ongoing efforts towards creating computational tools to elucidate insight and support engineering decisions in metabolism and protein design. We will highlight earlier contributions leveraging mathematical optimization to help reconstruct, analyze and redesign metabolic networks as well as design proteins with desired binding characteristics. We will next pivot towards more recent work that fuses both protein and metabolism design tasks to realize the assembly of carbon and energy efficient biosynthetic pathways by seamlessly blending known biochemical reactions with de novo steps. The emerging role of AI and large-language models will next be detailed in the context of enzyme kinetics and design prediction. Moving from stoichiometric to kinetic models of metabolism with genome-wide coverage, parameterization challenges will be described for several microbial production hosts. Insight and lessons gained from the application of these tools on a variety of biochemical engineering challenges that we have faced as members of the DOE bioenergy centers CBI and CABBI as well as the NSF AI Molecular Maker Laboratory Institute will be provided.