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Accelerated Adaptive Laboratory Evolution by Automated Repeated Batch Processes in Parallelized Bioreactors.

Lukas Bromig1, Dirk Weuster-Botz1

  • 1Chair of Biochemical Engineering, Technical University of Munich, Boltzmannstraße 15, D-85748 Garching, Germany.

Microorganisms
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

Adaptive laboratory evolution (ALE) accelerates microbial strain development. A novel bioreactor system significantly speeds up ALE experiments, offering a more efficient method for optimizing microbial cell factories.

Keywords:
Escherichia coliadaptive laboratory evolution (ALE)batch processbiomass estimatorblack box modelglycerol utilizationgrowth rate optimizationprocess automationprocess developmentrepeated batch process

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Area of Science:

  • Biotechnology
  • Microbial Engineering
  • Synthetic Biology

Background:

  • Adaptive laboratory evolution (ALE) is crucial for microbial strain development.
  • Traditional serial passaging methods are labor-intensive and data-limited.
  • Existing automated methods have limitations in scale and efficiency.

Purpose of the Study:

  • To develop a faster and more efficient ALE method.
  • To overcome limitations of shake flask and mL-scale automated systems.
  • To enable reliable data acquisition during microbial evolution experiments.

Main Methods:

  • Translation of shake flask ALE to a parallelized, L-scale stirred-tank bioreactor system.
  • Implementation of controlled, automated, repeated batch processes.
  • Utilizing off-gas analysis and a first-principles black-box model (soft sensor) for real-time biomass and growth rate estimation.

Main Results:

  • The novel bioreactor system achieved stable growth rates for *E. coli* on glycerol.
  • The method was 9.4 times faster than manual serial passaging and 3.6 times faster than mL-scale automated ALE.
  • Demonstrated the inadequacy of cumulative cell divisions (CCD) as a sole measure of evolutionary progress.

Conclusions:

  • The parallelized L-scale bioreactor system offers a significant advancement in ALE efficiency and data quality.
  • This automated approach accelerates the optimization of microbial cell factories.
  • Rethinking evolutionary timescales beyond CCD is necessary for accurate progress assessment.