BCAM Scientific Seminar: Towards a predictive synthetic biology enabled by machine learning and automation

Abstract: 
Biology has been transformed in the second half of the 20th century from a descriptive to a design science. We can engineer cells faster than ever, enabled by exponentially growing DNA synthesis and revolutionarily effective tools like CRISPR-enabled gene editing. However, while we can make the DNA changes we intend, the end result on cell behavior is usually unpredictable. In this talk, I will explain our efforts to create predictive algorithms that take -omics data and produce actionable items for bioengineering biofuel-producing cells. I will show how machine learning and mechanistic models, enabled by automation capabilities such as microfluidics, can produce predictions accurate enough to drive synthetic biology efforts. 

Short bio: 
Hector Garcia Martin was born in Bilbao. He studied physics and specialized in solid state physics at the UPV/EHU. His doctoral studies in condensed matter physics were performed at the University of Illinois at Urbana-Champaign, where he also worked in biological problems finding an explanation for one of the oldest patterns in biology: the species area relationship. His interest in biocomplexity and in using mathematical methods in microbial ecology drove him to work at the Department of Energy Joint Genome Institute, where he worked on metagenomics as a postdoctoral fellow. Seeking a more predictive framework for microbiology and microbial ecology he started work at the Joint BioEnergy Institute and Lawrence Berkeley National Lab with the intention to develop predictive models for pure cultures as well as microbial communities

Poster