Modeling and optimization of a multi-product biosynthesis factory for multiple objectives.

Area of Science:

• Metabolic Engineering
• Systems Biology
• Biotechnology

Background:

• Metabolic pathway optimization is crucial for efficient multi-product biosynthesis.
• Understanding flux ratios and enzyme kinetics is key to enhancing microbial production.

Purpose of the Study:

• To develop an augmented model for simultaneous optimization of serine and tryptophan flux ratios in Escherichia coli.
• To identify optimal metabolic pathway manipulations for maximizing amino acid biosynthesis.

Main Methods:

• Linked dynamic tryptophan operon, aromatic amino acid biosynthesis, and central carbon metabolism models.
• Estimated six new kinetic parameters using experimental data and published literature.
• Employed the elitist non-dominant sorting genetic algorithm (NSGA-II) with pattern recognition heuristics for multi-objective optimization.

Main Results:

• Successfully maximized biosynthesis rates of serine and tryptophan simultaneously.
• Obtained and elucidated Pareto-optimal enzyme activities for amino acid biosynthesis.
• Identified potential improvements in metabolic pathway recipes through multi-objective optimization.

Conclusions:

• The augmented model provides a robust framework for optimizing complex metabolic networks.
• Genetic algorithms and multi-objective optimization strategies are effective for enhancing microbial biosynthesis.
• The study highlights potential advancements in metabolic pathway engineering for industrial applications.