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Related Concept Videos

Bioreactor Design and Operational System01:29

Bioreactor Design and Operational System

220
Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
220
Bioreactor Controls-II01:18

Bioreactor Controls-II

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In aerobic fermentations, oxygen is vital for microbial growth and metabolite production. Since air comprises only about 20% oxygen and the gas is poorly soluble in water—just 9 ppm at 20°C—supplying sufficient oxygen becomes a critical challenge, especially in high-demand processes like yeast growth or citric acid production. Even a fully saturated broth may offer only a few seconds of oxygen availability.To address this, sterile or scrubbed air is introduced into the...
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Bioreactor Controls-III01:22

Bioreactor Controls-III

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Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
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Designing Growth Media for Bioreactors01:30

Designing Growth Media for Bioreactors

84
Growth media provide essential nutrients that support cell growth and metabolism, thereby enhancing the yield of valuable products such as enzymes, antibiotics, and biomass. Designing an effective growth medium involves balancing all components to prevent nutrient limitations or toxic excesses, both of which can impair growth and reduce product yields.Composition of a Typical Growth MediumA typical growth medium contains carbon and nitrogen sources, salts, vitamins, trace elements, and...
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Scale-Up Processes01:14

Scale-Up Processes

119
The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
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Upstream Processing01:27

Upstream Processing

102
Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...
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Related Experiment Video

Updated: May 5, 2026

Power Input Measurements in Stirred Bioreactors at Laboratory Scale
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A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential

Masih Karimi Alavijeh1,2, Yih Yean Lee3, Sally L Gras1,2

  • 1Department of Chemical Engineering The University of Melbourne Parkville Victoria Australia.

Engineering in Life Sciences
|July 8, 2024
PubMed
Summary

Machine learning (ML) models show promise for predicting bioreactor scale-up parameters in biopharmaceutical development. This approach aids in optimizing cell growth and scaling processes for monoclonal antibody production.

Keywords:
bioprocessingbioreactordata‐driven modelingmachine learningmammalian cell

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Area of Science:

  • Biopharmaceutical manufacturing
  • Process engineering
  • Computational biology

Background:

  • Bioreactor scale-up is a critical challenge in the biopharmaceutical industry, impacting process development and efficiency.
  • Traditional scale-up strategies often lack robustness, necessitating innovative approaches for reliable process transfer across scales.
  • Digital transformation offers new avenues for optimizing bioprocess development through advanced modeling techniques.

Purpose of the Study:

  • To evaluate the application of machine learning (ML) algorithms for bioreactor scale-up, focusing on predicting key scaling parameters.
  • To identify critical factors for developing effective ML models for bioprocess scale-up.
  • To assess the potential of ML in improving the prediction of cell growth and scaling parameters in bioreactor systems.

Main Methods:

  • Collated data from literature and public sources for bioreactor scale-up studies involving Chinese Hamster Ovary (CHO) cell-generated monoclonal antibody (mAb) products.
  • Developed unsupervised and supervised ML models, including artificial neural networks with embedding layers.
  • Conducted case studies to analyze the relationship between cell growth and scale-sensitive bioreactor features.

Main Results:

  • Identified similarities and differences in bioreactor performance across various scales, particularly between small- and large-scale systems.
  • An embedding layer significantly enhanced the predictive capability of artificial neural network models for large-scale cell growth by capturing process similarities.
  • Developed ML models capable of predicting critical scaling parameters, demonstrating ML's utility in assisting the scale-up process.

Conclusions:

  • ML algorithms offer a powerful tool to enhance bioreactor scale-up strategies in the biopharmaceutical industry.
  • The developed ML models can predict cell growth and scaling parameters, facilitating more effective process development.
  • Future advancements require larger, more diverse datasets with comprehensive characterization under varied operational conditions to further refine ML tools for bioreactor scaling.