Modeling and validation of autoinducer-mediated bacterial gene expression in
microfluidic environments |
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Authors: | Caitlin M Austin William Stoy Peter Su Marie C Harber J Patrick Bardill Brian K Hammer Craig R Forest |
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Institution: | 1.George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA;2.Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA;3.Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, USA;4.School of Biology, Georgia Institute of
Technology, Atlanta, Georgia 30332, USA |
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Abstract: | Biosensors exploiting communication within genetically engineered bacteria are becoming
increasingly important for monitoring environmental changes. Currently, there are a variety of
mathematical models for understanding and predicting how genetically engineered bacteria respond to
molecular stimuli in these environments, but as sensors have miniaturized towards microfluidics and
are subjected 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. We report a
mathematical model that accurately predicts the biological response of the autoinducer N-acyl
homoserine lactone-mediated green fluorescent protein expression in reporter bacteria in
microfluidic environments by accommodating these rate constants. This generalized mass action model
considers a chain of biomolecular events from input autoinducer chemical to fluorescent protein
expression through a series of six chemical species. We have validated this model against
experimental data from our own apparatus as well as prior published experimental results. Results
indicate accurate prediction of dynamics (e.g., 14% peak time error from a pulse input) and with
reduced mean-squared error with pulse or step inputs for a range of concentrations
(10 μM–30 μM). This model can help advance the design of
genetically engineered bacteria sensors and molecular communication devices. |
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