Improving metabolic engineering design with enzyme-thermo optimization.

Area of Science:

• Metabolic Engineering
• Systems Biology
• Computational Biology

Background:

• Classical stoichiometric algorithms in metabolic engineering often neglect thermodynamic feasibility and enzyme costs.
• This limitation impacts the predictive accuracy of the Design-Build-Test-Learn (DBTL) cycle.
• There is a need for improved computational frameworks to enhance metabolic engineering strategies.

Purpose of the Study:

• To introduce ET-OptME, a novel framework for metabolic engineering.
• To systematically incorporate enzyme efficiency and thermodynamic feasibility constraints into genome-scale metabolic models.
• To enhance the predictive performance and physiological realism of metabolic intervention strategies.

Main Methods:

• Developed ET-OptME by integrating two algorithms to address limitations of classical methods.
• Implemented a stepwise constraint-layering approach to manage thermodynamic bottlenecks and enzyme usage.
• Applied the framework to genome-scale metabolic models, specifically evaluating targets in Corynebacterium glutamicum.

Main Results:

• ET-OptME demonstrated significantly improved precision and accuracy compared to stoichiometric, thermodynamic, and enzyme-constrained methods.
• Quantitative evaluations showed substantial increases in minimal precision (up to 292%) and accuracy (up to 106%) for five product targets.
• The framework generated more physiologically realistic intervention strategies aligned with experimental data.

Conclusions:

• ET-OptME provides a more robust and predictive approach to metabolic engineering.
• The integration of thermodynamic and enzyme constraints enhances the DBTL cycle's effectiveness.
• This framework offers a valuable tool for designing efficient metabolic interventions and optimizing microbial cell factories.