Dynamics, Feedback, and Noise in Gene Expression
Although two microbes may have the same genetic code (like identical twins), the way they express their genes can generate differences between cells. This 'noise,' or variability in gene expression has important implications for clinically relevant problems like antimicrobial drug resistance.
For example, a subset of a population of bacteria may temporarily enter a state where they are resistant to toxins. By relying on this transient subpopulation of resistant cells, microbes use noise and random chance to ensure that the population survives occasional periods of stress.
We use time-lapse microscopy to quantitatively measure noise in living cells over many generations of growth.
Feedback Control Systems for Synthetic Biology
We are working on a collaborative project to design RNA-based PID controllers that make use of CRISPR/dCas9 and small RNA transcriptional regulators to programmably control gene expression. Control theory and dynamic modeling guide controller design and provide quantitative predictions of controller performance.
In a second project in this area we are building control systems to improve microbial biofuel production. Engineered microorganisms can break down sugars from biomass and convert them into biofuels such as alcohols, diesels, or jet fuels. However, a major challenge in microbial biofuel production is that biofuels are often toxic to cells. Thus, the more biofuel a cell produces, the less likely it is to survive. To overcome this limitation we are working on feedback control mechanisms for improving microbial biofuel tolerance. For example, efflux pumps are membrane transporters that can export biofuel. By using biosensors that respond to biofuel, we design feedback loops so that the pumps only turn on when fuel is present. These synthetic feedback loops can be used to optimize biofuel production, while minimizing the burden on the cell.
The Dunlop Lab uses approaches from Synthetic Biology and Systems Biology to quantitatively understand and engineer cellular processes. We are especially interested in how microbes use feedback, and also in engineering novel feedback loops. We use both experimental and theoretical approaches to study these complex biological systems.