Transfer Learning Approaches in Bioprocess Engineering: Opportunities and Challenges.

Area of Science:

• Bioprocess Engineering
• Machine Learning
• Data Science

Background:

• Data scarcity is a major challenge in bioprocess engineering, hindering model development and process optimization.
• Traditional modeling approaches often require extensive datasets, which are difficult and costly to obtain in bioprocessing.
• Transfer learning (TL) offers a novel solution by leveraging existing knowledge to build effective models with limited data.

Purpose of the Study:

• To critically review recent advancements in applying transfer learning (TL) within bioprocess engineering.
• To highlight the diverse applications of TL across various bioprocess domains.
• To identify current challenges and future research directions for TL in this field.

Main Methods:

• Review of recent literature on transfer learning applications in bioprocess engineering.
• Analysis of TL's impact on genomic analysis, bioreactor modeling, and chromatographic processes.
• Assessment of challenges such as data heterogeneity and model transferability.

Main Results:

• TL significantly enhances model accuracy for predicting protein functions, growth, and product formation.
• TL improves the prediction of retention times in chromatographic processes.
• Applications span from upstream (genomics) to downstream (chromatography) bioprocessing.

Conclusions:

• Transfer learning is a powerful tool for overcoming data scarcity in bioprocess engineering.
• Future work should focus on integrating TL with hybrid and physics-informed models and developing standardized datasets.
• TL facilitates the creation of more data-efficient, generalizable, and interpretable bioprocess models.