Programmed evolution for optimization of orthogonal metabolic output in bacteria.

Area of Science:

• Synthetic biology
• Metabolic engineering
• Evolutionary computation

Background:

• Metabolic engineering often faces challenges with the loss of desired metabolic output due to natural selection in microbial populations.
• Existing methods typically combat microbial evolution, rather than leveraging it for optimization.

Purpose of the Study:

• To introduce and demonstrate a novel approach called Programmed Evolution for optimizing metabolic pathways.
• To harness bacterial evolution as a computational tool for metabolic engineering.

Main Methods:

• Programmed bacteria with DNA code to compute solutions for metabolic pathway optimization.
• Applied Programmed Evolution to optimize caffeine to theophylline conversion in E. coli.
• Varied genetic elements including promoter strength, RBS, plasmid copy number, and chaperone proteins across 24 strains.
• Linked theophylline production to fitness using a theophylline riboswitch and tetracycline resistance.

Main Results:

• Observed a shift in genotype distribution within the bacterial population after selection.
• Demonstrated increased conversion of caffeine to theophylline in the most fit strains.
• Validated the proof-of-concept for Programmed Evolution in metabolic pathway optimization.

Conclusions:

• Programmed Evolution offers an alternative paradigm by utilizing, rather than combating, evolution in metabolic engineering.
• This modular system allows programming bacteria and employing evolution to identify and maintain optimal genetic elements for metabolic output.
• Potential applications span diverse fields including pharmaceuticals, energy, and bioremediation.