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Synthetic biology, by engineering biological systems for specific functions, can have widespread applications. For example, microorganisms can be engineered to produce valuable chemicals that are difficult to synthesize, or cells engineered to detect and respond to levels of glucose concentration by secreting insulin. Moreover, building simple circuits with well-characterized molecular components can teach us a lot about biology. These minimal circuits provide us with a tractable context where we can control all of the components and their interactions (much like a biological electronic breadboard), in addition to generating useful perturbations to probe biological systems. Using approaches inspired by physics, these minimalistic models can give us deeper insights into biological systems.


The Potvin lab research goals are to engineer reliable synthetic gene circuits suitable for impactful applications, and to use them as models and tools to learn more about biology.


See selected research interests and example approach below.



Cells use molecular signals to transmit information about their environment or to communicate with each other. It has become increasingly clear in recent years that these signals are not simply ‘ON’ or ‘OFF’, but that information can be encoded in the signal dynamics. For example, exposing cells to different environments can result in proteins pulsing at different frequencies, which ultimately results in different cell fates (schematic below). Synthetic gene circuits can provide simple models for signal encoders and decoders, and are promising tools to probe natural signal encoding.











Even though microbial species are typically studied in isolation, they typically live in complex multi-species ecosystems, such as surface biofilms or in the human gut. The collection of microbes that lives on human tissues (human microbiota) has been related to health and disease, from vitamin synthesis in the gut to inflammatory bowel disease. However, these ecosystems are composed of thousands of species and difficult to study. Using a bottom-up approach, building synthetic ecosystems under controlled conditions, will help us understand both the basic principles underlying these communities, and also how we can engineer them for specific applications.




Synthetic gene circuits made from well characterized parts can execute a variety of tasks, but they are still far from the accuracy of natural systems, such as the circadian oscillations in cyanobacteria. The first synthetic oscillator, the repressilator, used a simple design where three genes inhibit the production of each other in a single feedback loop, and displayed noisy oscillations. Subsequent oscillators introduced various features that marginally improved the precision, but the designs were still limited to differential equations analyses that mostly ignored the intrinsic challenges posed by stochastic chemistry. We took a different approach by going back to the original design – this time using theory to account for stochastic gene expression, as well as quantitative characterization in a microfluidic device – following hundreds of bacteria for hundreds of generations under constant growth conditions. We could thus systematically identify and eliminate sources of noise in the circuit.














The final streamlined circuit we designed peaks once every 14 generations, and the phase drift is so low that it takes more than 200 cell divisions to accumulate half a period of phase drift. The circuit keeps its 14-generation period in a variety of growth conditions, such as different temperatures and in conditioned medium from early stationary phase. The combination of robustness to growth conditions and low phase drift enables whole flasks of cells to oscillate in synchrony, without any coupling between cells. These findings revealed that even the simplest synthetic circuits can achieve a precision that rival natural systems, as long as stochastic theory is used in the design – which is particularly important for synthetic circuits which have not been shaped through evolution.













Our research is funded by:

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