Grainger College of Engineering, All Events

Abstract: Biomanufacturing has the alluring potential of providing enhanced-performance products, ameliorating supply chain disruptions, providing environmental benefits, addressing national security needs, and creating new jobs and industries. The demand for products that use biological processes and genetically modified microorganisms in place of traditional production methods is increasing: these products are estimated to have a market of ~$200B by 2040, if production costs can be significantly lowered. However, our inability to predict the outcome after a cell is modified makes synthetic biology a long and costly process that depends on arduously obtained expert biological knowledge. 

I will show how I am using what I learnt in my physics Ph. D. to start making synthetic biology as predictable as physics, and help unlock its full potential to nourish a thriving bioeconomy.

Bio: Hector Garcia Martin was born in Bilbao, part of the Basque region in Spain. Hector studied physics and specialized in solid state physics at the University of the Basque Country. He obtained his Ph. D in condensed matter physics from the University of Illinois at Urbana-Champaign, where he studied Bose Einstein Condensates and scaling laws in ecology. His interest in using theoretical physics tools to make biology predictable led him to join the Department of Energy Joint Genome Institute, where he worked on studying microbial communities through metagenomics as a postdoctoral fellow. Pursuing the opportunity to improve predictive models in biology through synthetic biology, he became a group lead at Berkeley Lab in 2007, where he is part of the Joint BioEnergy Institute and the Agile BioFoundry programs. In his current role, he combines machine learning, mechanistic models, automation, microfluidics and genetic editing techniques to effectively guide the metabolic engineering process and provide some of the first examples in predictive synthetic biology.