SUMMARY

Biosensors exploiting communication within genetically engineered bacteria are becoming increasingly important for monitoring environmental changes. Recently these sensors have miniaturized towards microfluidics due to the greater control they provide over things such as the population density and dynamic inputs. Although great strides have been made to study a single strain of bacteria in a microfluidic device, there is still a need to be able to study two populations of bacteria communicating with one another. Currently, there are a variety of mathematical models for understanding and predicting how genetically engineered bacteria respond to molecular stimuli in bulk culture environments, but when applied to microfluidics and to complex time-varying inputs, the shortcomings of these models have become apparent. The effects of microfluidic environments such as low oxygen concentration, increased biofilm, diffusion limited molecular distribution, and higher population densities strongly affect rate constants for gene expression not accounted for in previous models. In this work we developed a microfluidic platform capable of housing two bacteria populations to study the bacterial communication with dynamic control of inputs, long-term experimentation, and no cross contamination. We also developed a mathematical model that accurately predicts the biological response of the bacteria populations communicating in the microfluidic environment. This work can serve as a valuable tool in understanding genetically engineered bacteria and improving biosensor design capabilities, opening the door for sensors that adapt to environmental dynamics and communicate with each other.