The interrelationship between promoter strength, gene expression, and growth rate

Area of Science:

• Microbiology
• Synthetic Biology
• Metabolic Engineering

Background:

• Heterologous protein expression in bacteria impacts cellular growth rates.
• Quantitative models are needed to predict and engineer these effects for applications in synthetic biology and metabolic engineering.
• A previous scaling model predicted a linear relationship between microbial growth rate and heterologous protein expression based on optimal ribosome allocation.

Purpose of the Study:

• To rigorously validate the existing scaling model for heterologous protein expression and growth rate.
• To expand upon the existing theory by developing a promoter activity model.
• To predict the fitness cost associated with enzyme production for high-flux heterologous metabolic pathways.

Main Methods:

• Utilized a library of synthetic constitutive promoters to drive expression of eGFP and amiE proteins in E. coli.
• Quantified the fitness cost of gene expression across different bacterial strains and growth media.
• Developed and applied a promoter activity model to relate promoter strength to growth rate and protein expression.

Main Results:

• Demonstrated that the fitness cost of gene expression is consistent with the predictions of the scaling model across all tested conditions.
• Successfully validated the linear relationship between microbial growth rate and heterologous protein expression.
• Quantitatively predicted the relationship between basal promoter strength, growth rate, and protein expression.

Conclusions:

• The study validates a quantitative model for predicting the impact of heterologous protein expression on bacterial growth rates.
• A new promoter activity model allows for prediction of growth rate based on promoter strength and protein expression.
• Findings have broad implications for engineering microbial growth and optimizing protein production in applied biological sciences.