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Bioreactor Controls-I01:28

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Maintaining optimal conditions within fermenters is essential for maximizing microbial productivity and ensuring process efficiency. This lesson focuses on key parameters—temperature, foam, pH, carbon dioxide, oxygen, and pressure—and their precise measurement and control strategies in fermentation systems.Temperature ControlTemperature regulation is critical due to the exothermic nature of many fermentation processes. In small laboratory fermenters, temperature is commonly monitored using...

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MIFE and MIFD: Minimum information for fermentation experiments and devices.

Georgios K Georgakilas1, Brett Metcalfe2,3, Ariane Bize4

  • 1Information Management Systems Institute, ATHENA Research Center, 15125 Marousi, Greece.

Gigascience
|April 12, 2026
PubMed
Summary
This summary is machine-generated.

New minimum information models for fermentation experiments (MIFE) and devices (MIFD) enhance data management in industrial biotechnology. These models promote FAIR data principles, accelerating AI-driven bioprocess discovery and innovation.

Keywords:
FAIR principlesbiomanufacturingdata AI-readinessdata managementfermentation devicesfermentation experimentsindustrial biotechnologymetadatametadata standardizationminimum information models

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Area of Science:

  • Industrial Biotechnology
  • Data Science
  • Bioinformatics

Background:

  • Advancements in 21st-century technology are driving industrial biotechnology (IB) towards Big Data innovation.
  • Effective data management, annotation, and standardization are crucial for IB's Big Data era.
  • Minimum Information Models (MIMs) are established data management tools providing standardized metadata recording.

Purpose of the Study:

  • Introduce MIFE and MIFD, minimum metadata sets for IB fermentation experiments.
  • Facilitate data management and annotation tailored to IB fermentation processes.
  • Integrate Findable, Accessible, Interoperable, and Reproducible (FAIR) principles into IB.

Main Methods:

  • Developed MIFE and MIFD based on established models and community standards.
  • Ensured MIFE and MIFD facilitate integration with existing infrastructure and community adoption.
  • Integrated MIFE and MIFD with FAIR Data Station (FAIR DS) for metadata validation and automated data uptake.
  • Created a Python package for programmatic use of MIFE and MIFD via a Linked Data Modeling Language (LinkML) schema.

Main Results:

  • MIFE and MIFD provide a standardized approach to metadata for IB fermentation experiments.
  • The proposed models are compatible with existing infrastructure and community standards.
  • Demonstrated automated metadata validation and data uptake using FAIR DS and FAIRDOM-SEEK.
  • A Python package facilitates the programmatic application of MIFE and MIFD for data analysis.

Conclusions:

  • MIFE and MIFD simplify knowledge discovery in industrial biotechnology.
  • These models can accelerate the application of AI and explainable AI (XAI) in bioprocesses.
  • Promoting standardized data management will enhance understanding and scaling of bioprocesses.