Twelve quick tips for designing sound dynamical models for bioprocesses

Area of Science:

• Biotechnology
• Biochemical Engineering
• Process Systems Engineering

Background:

• Bioprocesses are inherently complex, driving the increasing use of mathematical models for design, control, and optimization.
• These models typically integrate biochemical reactions, mass balance principles, and empirical kinetics.
• Biological systems are dynamic and adaptive, leading to nonlinear model behaviors.

Purpose of the Study:

• To highlight the critical role of mathematical modeling in understanding and managing complex bioprocesses.
• To emphasize the importance of balancing model complexity with practical usability.
• To advocate for tailored modeling approaches rather than universal solutions.

Main Methods:

• Derivation of model equations from mass balance and empirical kinetics.
• Analysis of nonlinear dynamics inherent in biological systems.
• Discussion of model calibration challenges and significance.

Main Results:

• Mathematical models are essential tools for bioprocess design, control, and optimization.
• A key challenge in biological modeling is maintaining a balance between biological realism and model tractability.
• Overly complex models risk poor calibration and reduced practical significance.

Conclusions:

• Effective bioprocess modeling requires a careful balance between complexity and ease of use.
• Model development should be guided by specific application objectives and environmental contexts.
• A 'one-size-fits-all' universal model is generally not optimal for diverse bioprocess applications.