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Online bioprocess data generation, analysis, and optimization for parallel fed-batch fermentations in milliliter

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  • 1Chair of Bioprocess Engineering Institute of Biotechnology Technische Universität Berlin Berlin Germany.

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|July 7, 2020
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Summary

This study presents an automated mini-bioreactor system for efficient bioprocess optimization. The integrated liquid handling station enables dynamic control and accurate parameter estimation in Escherichia coli cultivations.

Keywords:
Automated data processingBioprocess optimizationLaboratory automationMini‐bioreactorsScheduling

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Area of Science:

  • Biotechnology
  • Bioprocess Engineering
  • Synthetic Biology

Background:

  • Mini-bioreactor systems are crucial for bioprocess development, requiring precise control over process parameters and high data acquisition.
  • Dynamic process condition adjustments and efficient data management are essential for optimizing microbial cultivations.
  • Current limitations in mini-bioreactor automation hinder rapid optimization and accurate model parameter estimation.

Purpose of the Study:

  • To develop and validate an integrated automated system for mini-bioreactor cultivation and process optimization.
  • To enable dynamic control and online experimental design for enhanced bioprocess development.
  • To improve the accuracy of Escherichia coli fed-batch model parameter estimation through an automated platform.

Main Methods:

  • Integration of a parallel mini-bioreactor system with a liquid handling station (LHS) for automated sample collection and reagent addition.
  • Utilized non-invasive sensors for online monitoring of pH and dissolved oxygen (DO).
  • Implemented an online experimental re-design method coupled with LHS control for dynamic process adjustments and fed-batch conditions via enzymatic glucose release.

Main Results:

  • Successfully automated data collection, analysis, and formalization for bioprocess control and optimization.
  • Demonstrated the application of an online experimental re-design method in eight Escherichia coli fed-batch cultivations.
  • Achieved accurate estimation of Escherichia coli fed-batch model parameters by dynamically optimizing experimental designs online.

Conclusions:

  • The presented integrated system significantly enhances automation and control capabilities in mini-bioreactor scale bioprocesses.
  • The automated platform facilitates advanced control and optimization strategies, enabling efficient bioprocess development.
  • This approach provides a robust framework for high-accuracy model parameter estimation and accelerates microbial strain and process optimization.