Optimization of bioprocess productivity based on metabolic-genetic network models with bilevel dynamic programming

Area of Science:

• Metabolic Engineering
• Systems Biology
• Biotechnology

Background:

• Metabolic engineering aims to increase microbial product yields via genetic modifications.
• Dynamic strategies, controlling gene expression over time, outperform static methods by balancing growth and production.
• Temporal regulation is key to resolving the trade-off between cellular growth and product formation.

Purpose of the Study:

• To develop and apply a bilevel optimization framework for dynamic metabolic engineering.
• To identify optimal dynamic genetic and process manipulations for increased microbial productivity.
• To maximize ethanol yield in a batch fermentation process using Escherichia coli.

Main Methods:

• Utilized a bilevel optimization framework integrated with constraint-based models.
• Employed dynamic enzyme-cost flux balance analysis (deFBA) to model metabolic network dynamics.
• Applied the framework to optimize temporal regulation of gene expression and process parameters.

Main Results:

• The computational framework successfully identified optimal dynamic strategies for ethanol production.
• Demonstrated the effectiveness of dynamic control over static genetic modifications.
• Highlighted the critical role of integrating genetic regulation with enzyme production and degradation dynamics.

Conclusions:

• Dynamic metabolic engineering, incorporating temporal control, is superior for enhancing microbial product formation.
• The developed bilevel optimization framework provides a powerful tool for designing optimal dynamic strategies.
• Integrating genetic and enzyme-level dynamics is essential for maximizing productivity in engineered microbes.