In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven...

Area of Science:

• Biotechnology
• Biochemical Engineering
• Microbial Physiology

Background:

• Industrial bioreactors (10,000–700,000 L) exhibit heterogeneous zones (substrate, dissolved gas, pH) due to scale-up constraints.
• Microbial populations fluctuate, facing dynamic micro-environmental conditions that alter metabolism and gene expression.
• Heterogeneity in large bioreactors can reduce microbial production compared to ideal lab-scale, well-mixed conditions.

Purpose of the Study:

• To highlight the need for predictive tools to quantify the impact of bioreactor heterogeneities on microbial production.
• To advocate for integrating microbial kinetics simulations with large-scale environmental condition calculations for accurate bioreactor performance prediction.
• To present methodologies for developing microbial models and hydrodynamic conditions for comprehensive bioreactor modeling.

Main Methods:

• Reviewing existing literature on bioreactor scale-up challenges and microbial responses.
• Discussing the combination of microbial kinetic modeling with computational fluid dynamics (CFD) for hydrodynamic modeling.
• Outlining experimental procedures for deriving microbial models and hydrodynamic parameters.

Main Results:

• The study confirms that bioreactor scale-up introduces significant environmental heterogeneities affecting microbial performance.
• Integrating microbial kinetics with hydrodynamic simulations provides a powerful predictive framework for large-scale bioprocesses.
• Identification of gene regulatory networks is crucial for implementing advanced predictive models.

Conclusions:

• Combining microbial kinetics and hydrodynamic simulations is essential for predicting and optimizing performance in large industrial bioreactors.
• Accurate modeling requires robust microbial models and detailed understanding of bioreactor hydrodynamics.
• Future research should focus on incorporating gene regulatory networks into these integrated models for enhanced predictive power.