Modeling and validation of autoinducer-mediated bacterial gene expression in microfluidic environments

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...

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Bibliographic Details
Published in:Biomicrofluidics 2014-05, Vol.8 (3), p.034116-034116
Main Authors: Austin, Caitlin M, Stoy, William, Su, Peter, Harber, Marie C, Bardill, J Patrick, Hammer, Brian K, Forest, Craig R
Format: Article
Language:English
Subjects:
Bacteria
Biosensors
Constants
Design engineering
Diffusion rate
Electronic devices
Environmental monitoring
Fluorescence
Gene expression
Genetic engineering
Mathematical analysis
Microfluidics
Organic chemistry
Predictions
Protein expression
Proteins
Rate constants
Regular
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Summary: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.
ISSN:1932-1058
1932-1058
DOI:10.1063/1.4884519