By modifying the genomes of plants and microorganisms, synthetic biologists can design biological systems that meet a specification, such as producing valuable chemical compounds, making bacteria sensitive to light, or programming bacterial cells to invade cancer cells. This field of science, though only a few decades old, has enabled large-scale production of medical drugs and established the ability to manufacture petroleum-free chemicals, fuels, and materials. It seems that biomanufactured products are here to stay, and that we will rely on them more and more as we shift away from traditional, carbon-intensive manufacturing processes.
But there is one big hurdle – synthetic biology is labor intensive and slow. From understanding the genes required to make a product, to getting them to function properly in a host organism, and finally to making that organism thrive in a large-scale industrial environment so it can churn out enough product to meet market demand, the development of a biomanufacturing process can take many years and many millions of dollars of investment.
Héctor García Martín, a staff scientist in the Biological and Systems Engineering (BSE) Division, is working to accelerate and refine this R&D landscape by applying artificial intelligence and the mathematical tools he mastered during his training as a physicist.
Berkeley Lab spoke with him to learn how AI, bespoke algorithms, mathematical modeling, and robotic automation can come together as a sum greater than its parts, and provide a new approach for synthetic biology.
