I am a scientist in Berkeley Lab working on bioengineering cells to produce biofuels and other renewable products for the benefit of society.
I develop predictive mathematical models of cells, in the intersection of machine learning, synthetic biology and automation, as the founder of Berkeley Lab's Quantitative Metabolic Modeling group, and Director for JBEI's Data Science and Modeling at the Biofuels and Bioproducts Division.
I am a physicist working in synthetic biology. How did I get here?
I started my Ph. D. in condensed matter physics, but very early on became fascinated by emergent properties in biology, like e.g. the species area rule. Hence, I decided to work on metagenomics for my postdoc at the Joint Genome Institute, obtaining blueprints of microbial community metabolism. Irked by the difficulty of creating predictive models for microbial communities, I focused on an easier target: pure cultures for which we have the tools to change their genome. This took me to my current position, developing predictive models for biological systems to produce renewable biofuels and other products. You can read the expanded version of this story here.
Hector in the non-technical media
Machine Learning Takes on Synthetic Biology: Algorithms Can Bioengineer Cells for You
Berkeley Lab scientists develop a tool that could drastically speed up the ability to design new biological systems (Berkeley Lab News).