Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling

Area of Science:

• Bioprocess Engineering
• Systems Biology
• Mathematical Biology

Background:

• Cellular heterogeneity significantly impacts bioprocess performance, but its effects are not fully understood.
• Accurate analytical and mathematical models are crucial for designing and operating efficient bioprocesses.
• Current methods often lack the high-throughput and speed required for dynamic bioprocess monitoring.

Purpose of the Study:

• To provide an overview of single-cell analysis techniques and mathematical modeling frameworks for bioprocess control and optimization.
• To highlight methods suitable for monitoring microbial processes under dynamic conditions.
• To emphasize the potential of integrating experimental data with advanced modeling for improved process design and scale-up.

Main Methods:

• Review of single-cell analysis techniques, focusing on high-throughput methods like flow cytometry.
• Discussion of mathematical modeling frameworks, including population balance models and individual-based models.
• Exploration of integrating experimental data with computational models.

Main Results:

• Flow cytometry is identified as a suitable high-throughput method for dynamic bioprocess monitoring.
• Individual-based models show strong potential for integrating diverse experimental data.
• A rigorous modeling approach can prevent performance losses during process design and scale-up.

Conclusions:

• Integrating single-cell analysis with advanced mathematical modeling, particularly individual-based models, offers a pathway to better understand and control cellular heterogeneity in bioprocesses.
• This integrated approach is essential for optimizing bioprocess performance, facilitating scale-up, and advancing synthetic biology applications.
• The described methodologies can lead to more predictable and efficient bioprocesses, minimizing performance issues.