Prof. Voigt obtained his Bachelor’s degree in Chemical Engineering at the University of Michigan, Ann Arbor and a PhD in Biochemistry and Biophysics at the California Institute of Technology. He continued his postdoctoral research in Bioengineering at the University of California, Berkeley. His academic career commenced as an Assistant and Associate Professor at the Department of Pharmaceutical Chemistry at the University of California-San Francisco. Chris Voigt joined the Department of Biological Engineering at MIT as Associate Professor in 2011.
His lab is roughly divided into two groups. The first is focused on the development of a programming language for cells. A genetic program consists of a combination of genetic circuits, each of which uses biochemistry to replicate a function analogous to an electronic circuit (e.g., a logic gate). Combining circuits yields more complex signal processing operations. Our near-term objective is to develop the foundations by which 20-30 circuit programs can be reliably built. This will require new classes of circuits that can be rapidly connected and are sufficiently simple and robust to be assembled by computer algorithms. We are also developing biophysical models that can map the sequence of a genetic part (e.g., a ribosome binding site) to its function. These models can be used to connect and optimize circuits and programs.
The second group in his lab is focused on applying these tools to problems in biotechnology. This encompasses new approaches to old problems (e.g., nitrogen fixation) as well as more futuristic ideas (e.g., re-programming bacteria as a drug delivery device). Currently, we are focused on harnessing the functions encoded within prokaryotic gene clusters. These are contiguous stretches of DNA in the genome that (ideally) contain all of the genes necessary and sufficient for that function. These clusters consist of diverse functions requiring ~20+ genes, including elaborate nano-machines and metabolic pathways. We are applying principles from synthetic biology to rebuild these functions from the ground up, in order to eliminate complex and often uncharacterized native regulation, gain complete control and understanding of the cluster, and to facilitate its optimization and transfer between organisms. To do this, we use the same computational tools, genetic circuits, and construction methodologies developed by the foundational half of the lab. This work represents a step towards whole genome design, where our vision is that the future designer would mix-and-match modular clusters to build a synthetic organism.