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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Advisor: Craig Forest, PhD (Georgia Institute of Technology)
Committee:
Brian Hammer, PhD (Georgia Institute of Technology)
Faramarz Fekri, PhD (Georgia Institute of Technology)
Mark Styczynski, PhD (Georgia Institute of Technology)
Peter Hesketh, PhD (Georgia Institute of Technology)
Dynamics of Molecular Communication in Bacteria within Microfluidic Environments
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 encapsulation, diffusion limited molecular distribution, and higher population densities strongly affect rate constants for gene expression not accounted for in previous models. Thus, the long-term goal of this research is to develop a microfluidic platform capable of housing two bacteria populations to study the bacterial communication with dynamic control the inputs, long-term experimentation, and no cross contamination. We also look to create a mathematical model that accurately predicts the biological response of the bacteria populations communicating in the microfluidic environment.
