Can cells be programmed like robots?

Christopher Voight says we can program cells by combining commands encoded in their DNA. He visits the Complex on June 16 in the next q-bios public lecture series to discuss the beginnings of a programming language for bacteria that he is developing at the University of San Fransisco’s Voight Labs and how it can be applied to problems in energy, materials, and drug production.

Voight is developing a basis by which cells can be programmed like robots to perform complex, coordinated tasks for pharmaceutical and industrial applications. He says, “We are engineering new sensors that give bacteria the senses of touch, sight, and smell. Genetic circuits — analogous to their electronic counterparts — are built to integrate the signals from the various sensors. Finally, the output of the gene circuits is used to control cellular processes. We are also developing theoretical tools from statistical mechanics and non-linear dynamics to understand how to combine genetic devices and predict their collective behavior.”

For an example, orthogonal green and red light sensors have been constructed to operate in E. coli. When an image is projected on a lawn of bacteria, the sensors are able to record the image as a pattern of gene expression. Voight is using the bacteria as a platform to combine simple genetic circuits to reconstruct signal processing algorithms. The bacteria present the results of the computation to the user as a visible, printed output at a macroscopic scale. in his talk, Voight will describe how this has inspired new computational methods to connect and optimize genetic circuits. His work also explores the design principles by which simple genetic circuits can be combined to produce complex functions. It can alsobe used to program cells to produce fuels, materials, and drugs.

Voight is an assistant professor in the pharmaceutical chemistry department of the University of California – San Francisco. He is the principle investigator at the university’s Voight Lab where he explores the use of genetic circuits as an analog to electronic circuitry.