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Optimization and Scale-Up of Fermentation Processes Driven by Models.

Yuan-Hang Du1, Min-Yu Wang2, Lin-Hui Yang1

  • 1School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China.

Bioengineering (Basel, Switzerland)
|September 22, 2022
PubMed
Summary
This summary is machine-generated.

This review guides selecting biological models for industrial fermentation, covering kinetic, constraint-based, and machine learning approaches. It also discusses hybrid models and scale-up predictions using computational fluid dynamics.

Keywords:
computational fluid dynamicsdata-drivenhybrid modelingmechanistic modelingscale-up

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Area of Science:

  • Biotechnology
  • Synthetic Biology
  • Metabolic Engineering

Background:

  • Industrial fermentation for sustainable compound production requires efficient strains, optimized conditions, and scalable processes.
  • Biological models are crucial for guiding strain development and process optimization in cell factories.
  • Selecting appropriate biological models is key to accelerating the development of industrial fermentation.

Purpose of the Study:

  • To provide guidance for the rapid selection of biological models for industrial fermentation.
  • To review mechanistic and data-driven modeling approaches applicable to cell factory development.
  • To discuss strategies for predicting bioreactor scale-up and culture behavior.

Main Methods:

  • Introduction to mechanistic modeling: kinetic modeling and constraint-based modeling (CBM).
  • Review of data-driven modeling using machine learning (ML) algorithms and their application scope.
  • Discussion on hybrid models combining ML and CBM.

Main Results:

  • Mechanistic and data-driven models offer distinct advantages for understanding and optimizing cell factories.
  • Hybrid models integrating ML and CBM can provide more comprehensive insights.
  • Integration of biological and computational fluid dynamics (CFD) models aids in predicting scale-up and culture behavior.

Conclusions:

  • The selection of appropriate biological models is critical for efficient industrial fermentation and sustainable development.
  • Combining different modeling approaches, including ML and CFD, enhances the prediction of fermentation processes and scale-up.
  • This review serves as a guide for researchers and engineers in choosing and applying biological models for cell factory optimization.