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Digital models in biotechnology: Towards multi-scale integration and implementation.

Fabian S F Hartmann1, Isuru A Udugama2, Gerd M Seibold1

  • 1Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark.

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Summary
This summary is machine-generated.

Digitalization is transforming industrial biotechnology by integrating digital models across scales. This study classifies current modeling approaches and discusses coupling cell and computational fluid dynamics models for optimizing bio-based production.

Keywords:
Data collectionMathematical modellingModel validationMulti-scaleSegregation

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Area of Science:

  • Industrial biotechnology
  • Digitalization in bioprocesses
  • Multiscale modeling

Background:

  • Industrial biotechnology leverages diverse research areas.
  • Digitalization is a key trend, driving the adoption of digital models.
  • Modeling is crucial for improving industrial biotechnology processes.

Purpose of the Study:

  • To systematically classify state-of-the-art modeling concepts in industrial biotechnology.
  • To critically discuss the advantages and limitations of current digital models.
  • To analyze strategies for coupling cell and computational fluid dynamics (CFD) models.

Main Methods:

  • Systematic classification of modeling concepts across different scales.
  • Critical analysis of digital model integration and application.
  • Evaluation of strategies for coupling cell models with CFD for bioprocesses.
  • Discussion of scale-down concepts for capturing intracellular data.

Main Results:

  • Digital models are increasingly applied across industrial biotechnology scales.
  • Coupling cell models with CFD is vital for studying microorganism performance in large-scale bioprocesses.
  • Gathering intracellular data under industrial conditions remains a challenge.
  • Scale-down approaches combined with analytical tools can capture single-cell intracellular states.

Conclusions:

  • Digital models offer significant potential for cell factory design and process optimization.
  • Further development is needed to integrate diverse modeling approaches for industrial applications.
  • Bridging the gap between intracellular data and large-scale bioprocess modeling is essential for future advancements.