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Howard H Chou1, Jay D Keasling

  • 11] UCSF-UCB Joint Graduate Group in Bioengineering, University of California, Berkeley, California 94720, USA [2] Joint BioEnergy Institute, Emeryville, California 94720, USA [3] Synthetic Biology Engineering Research Center, University of California, Berkeley, California 94720, USA.

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This study introduces feedback-regulated evolution of phenotype (FREP), a novel system that dynamically adjusts mutation rates to engineer biological traits. FREP accelerates the evolution of desired phenotypes by increasing diversity when needed and conserving it once achieved.

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Area of Science:

  • Synthetic biology
  • Metabolic engineering
  • Evolutionary biology

Background:

  • Engineering novel phenotypes in complex biological systems is challenging, particularly without high-throughput screening.
  • Natural evolution relies on increased mutation rates to generate diversity for adaptation.
  • Existing methods lack the ability to dynamically control mutation rates based on specific cellular needs.

Purpose of the Study:

  • To develop an adaptive control system for engineering novel phenotypes in biological systems.
  • To create a method that dynamically regulates mutation rates to facilitate desired evolutionary outcomes.
  • To enable the evolution of traits lacking natural sensors through synthetic biology approaches.

Main Methods:

  • Constructed a feedback-regulated evolution of phenotype (FREP) system utilizing a sensor and an actuator to control mutation rates.
  • Developed a framework for assembling synthetic transcription factors using metabolic enzymes to create novel biosensors.
  • Engineered four distinct sensors capable of detecting isopentenyl diphosphate in bacteria and yeast.

Main Results:

  • Successfully implemented the FREP system to dynamically control mutation rates in response to metabolite concentrations.
  • Demonstrated the ability to evolve increased production of specific metabolites, including tyrosine and isoprenoids.
  • Validated the efficacy of synthetic biosensors for metabolite detection in microbial hosts.

Conclusions:

  • FREP provides a powerful platform for rationally engineering complex phenotypes by controlling evolutionary processes.
  • Synthetic transcription factor assembly offers a versatile strategy for creating custom biosensors for metabolic engineering.
  • This approach significantly advances the potential for directed evolution in microorganisms for biotechnological applications.