UC Berkeley

Living organisms exhibit many fascinating behaviors that profoundly impact the world surrounding us. Some of these functions have been harnessed within biotechnology and redirected towards solving problems of industrial relevance, e.g. the microbial synthesis of pharmaceuticals and biofuels. However, these behaviors tend to be encoded by relatively simple genetics and constrained to a handful of lab-friendly organisms. To gain access to diverse, more complex behaviors, I have developed a number of methods for understanding and reprogramming sophisticated genetic networks inside cells. First, I reverse engineered and predictively modified the control of the Type III secretion system in Salmonella using mathematical modeling and high-throughput gene expression measurements in single cells. Next, a "refactoring" methodology was developed to entirely reconstruct and specify the genetics of a complex behavior using synthetic parts. I applied this strategy to reprogram and modularize the agriculturally relevant behavior of nitrogen fixation from Klebsiella. Then, classical engineering methods for nonlinear systems optimization were adapted to guide the selection of parts and optimize performance in well-specified genetic systems. Finally, orthogonal genetic wires were developed to engineer multiple behaviors in a single cell. I utilized these wires to construct orthogonal networks for sensing environmental conditions, performing computation, and synthesizing red and green pigments in E. coli. Together, these techniques and engineering principles represent a major step towards the design and synthesis of entire genomes based solely on genetic information found in sequence databases.