Harnessing bioreactor heterogeneity: From gradient understanding to autonomous control via multiscale modeling and...

Area of Science:

• Biotechnology
• Chemical Engineering
• Systems Biology

Background:

• Biomanufacturing scale-up is limited by the "scale-up effect," caused by environmental heterogeneities in large bioreactors.
• These heterogeneities stem from complex interactions between fluid dynamics and cellular physiology.
• Current approaches often passively observe these effects, hindering process optimization.

Purpose of the Study:

• To propose a framework for shifting biomanufacturing control from passive observation to active, intelligent management.
• To analyze how environmental gradients influence cellular behavior and physiology.
• To outline a roadmap towards autonomous, efficient, and sustainable biomanufacturing.

Main Methods:

• Analysis of environmental gradients and their impact on "cellular lifelines" and physiological responses.
• Application of multiscale modeling, integrating computational fluid dynamics (CFD) with physiological models.
• Exploration of a hybrid "mechanism-data symbiotic" modeling approach using artificial intelligence (AI).

Main Results:

• Environmental gradients create distinct "cellular lifelines," leading to varied physiological responses and population heterogeneity.
• Multiscale modeling facilitates a shift from gradient elimination to gradient exploitation for optimized scale-up.
• AI integration with mechanistic models enables real-time, adaptive optimization of bioprocesses.

Conclusions:

• The digital twin concept represents a closed-loop autonomous system for bioreactors, enabling perception, learning, and self-optimization.
• Overcoming challenges in model generalizability and data integration is key to realizing autonomous biomanufacturing.
• This paradigm shift promises more efficient and sustainable biomanufacturing processes.