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

Area of Science:

• Synthetic Biology
• Biophysics
• Environmental Monitoring

Background:

• Genetically engineered bacteria are crucial for environmental monitoring biosensors.
• Existing models fail to predict bacterial responses in miniaturized microfluidic systems with complex inputs.
• Microfluidic conditions like low oxygen and diffusion limits impact gene expression rates.

Purpose of the Study:

• To develop a mathematical model predicting bacterial responses in microfluidics.
• To account for microfluidic environmental factors affecting gene expression rate constants.
• To enhance the design of bacterial biosensors and molecular communication devices.

Main Methods:

• Developed a generalized mass action model.
• Modeled N-acyl homoserine lactone-mediated green fluorescent protein expression.
• Considered six chemical species in a biomolecular event chain.

Main Results:

• The model accurately predicts bacterial responses in microfluidic environments.
• Validated against experimental data, showing 14% peak time error for pulse inputs.
• Reduced mean-squared error for pulse and step inputs across various concentrations (10–30 μM).

Conclusions:

• The generalized mass action model accurately predicts biosensor behavior in microfluidics.
• This model enhances the reliability and design of engineered bacteria sensors.
• It facilitates advancements in molecular communication devices and environmental monitoring tools.