Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling

Area of Science:

• Synthetic biology
• Biotechnology
• Metabolic engineering

Background:

• Whole cell biosensors link chemical stimuli to gene expression for sensing.
• Optimizing biosensor regulatory components is challenging due to complex gene expression requirements.
• Classical methods are inefficient for exploring multidimensional experimental spaces.

Purpose of the Study:

• To apply a design of experiments (DoE) methodology for efficient mapping of gene expression levels in whole cell biosensors.
• To enhance biosensor performance by systematically optimizing regulatory components.
• To improve biosensors for detecting lignin biomass breakdown products like protocatechuic acid and ferulic acid.

Main Methods:

• Employed a design of experiments (DoE) methodology to systematically explore gene expression parameters.
• Applied DoE to two whole cell biosensors responding to protocatechuic acid and ferulic acid.
• Modified biosensor dose-response curves by adjusting regulatory components.

Main Results:

• Achieved significant improvements in biosensor performance metrics.
• Increased maximum signal output by up to 30-fold.
• Enhanced dynamic range (>500-fold), sensing range (~4-orders of magnitude), and sensitivity (>1500-fold).
• Modulated dose-response curves to achieve both digital and analog behaviors.
• Demonstrated successful optimization of biosensors for lignin-derived compounds.

Conclusions:

• Design of experiments (DoE) provides an efficient approach for optimizing whole cell biosensors.
• This methodology enables fine-tuning of biosensor characteristics, including sensitivity, dynamic range, and response type.
• The DoE method shows significant promise for optimizing novel regulatory systems and metabolic pathways.