A Paradigm of Computer Vision and Deep Learning Empowers the Strain Screening and Bioprocess Detection

Area of Science:

• Biotechnology
• Computer Science
• Machine Learning

Background:

• Efficient biomanufacturing relies on high-performance strains and optimized fermentation processes.
• Conventional offline detection methods are slow and unstable, impeding the Design-Build-Test-Learn cycle.
• A need exists for rapid, stable, and automated methods for strain screening and process optimization.

Purpose of the Study:

• To develop and validate an innovative research paradigm combining computer vision and deep learning for biomanufacturing.
• To create a practical framework for efficient strain selection and fermentation process optimization.
• To enable real-time monitoring and rapid detection in bioprocesses.

Main Methods:

• Developed a framework using computer vision to extract color space components from cultivation systems.
• Integrated data preprocessing with a 1D-CNN deep learning model for titer prediction.
• Applied Z-score preprocessing for enhanced model performance.

Main Results:

• Achieved a high correlation coefficient (R² = 0.9862) for gentamicin C1a titer prediction.
• Successfully applied the model for high-yield strain screening and real-time fermentation monitoring.
• Extended the application to rapid detection of fluorescent protein expression in promoter library construction.

Conclusions:

• The proposed visual sensing paradigm offers a theoretical framework for digital monitoring of bioprocesses.
• This approach facilitates standardization and efficiency in strain development and fermentation optimization.
• Computer vision and deep learning provide powerful tools for advancing biomanufacturing.