Improving control in microbial cell factories: from single-cell to large-scale bioproduction

Area of Science:

• Biotechnology
• Metabolic Engineering
• Systems Biology

Background:

• Bioprocess deviations commonly occur across various scales, leading to substrate alterations and affecting product synthesis.
• Correlating substrate uptake rate (qS) and product formation rate (qP) is crucial for bioproprocess observability and control.
• Current metabolic flux models can predict metabolic switches but often neglect biological noise from phenotypic and genotypic heterogeneity.

Purpose of the Study:

• To highlight the limitations of existing metabolic flux models in incorporating biological noise.
• To emphasize the importance of addressing heterogeneity for improved bioprocess observability and control.
• To discuss the implications of these limitations for metabolic engineering and bioprocess scale-up.

Main Methods:

• Review and analysis of existing metabolic flux modeling approaches.
• Identification of current model limitations regarding biological noise.
• Discussion of the impact of heterogeneity on bioprocess modeling.

Main Results:

• Existing metabolic flux models do not adequately incorporate biological noise (phenotypic and genotypic heterogeneity).
• This omission impairs the ability to accurately observe and control bioprocesses.
• Limitations hinder the practical application of fundamental biological network knowledge.

Conclusions:

• Accurate bioprocess modeling requires the integration of biological noise.
• Improved observability and control are essential for effective metabolic engineering and bioprocess scale-up.
• Future models must account for cellular heterogeneity to enhance bioprocess predictability and application.