Automatic construction of metabolic models with enzyme constraints

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Constraint-based metabolic models are crucial for understanding cellular metabolism.
• Existing methods like MOMENT and GECKO integrate enzyme kinetic (kcat) and mass constraints to improve model accuracy.
• However, these methods can increase model complexity and lack automated generation tools.

Purpose of the Study:

• To develop a simplified and automated approach for creating enzyme-constrained metabolic models.
• To improve the accuracy and predictive power of constraint-based models by incorporating enzyme limitations.
• To facilitate the routine construction and analysis of these advanced models.

Main Methods:

• Introduced short MOMENT (sMOMENT), a simplified version of MOMENT requiring fewer variables for direct enzyme constraint integration.
• Developed the AutoPACMEN toolbox for automated generation of sMOMENT-enhanced stoichiometric models, including data retrieval and model reconfiguration.
• Implemented tools for calibrating sMOMENT model parameters (kcat, enzyme pool) using flux data.

Main Results:

• sMOMENT provides equivalent predictions to MOMENT with reduced complexity.
• AutoPACMEN automates the creation of enzyme-constrained models from stoichiometric models and databases.
• Application to E. coli iJO1366 model demonstrated improved flux predictions, explaining metabolic switches and altering metabolic engineering strategies.

Conclusions:

• The sMOMENT approach and AutoPACMEN toolbox simplify the construction and analysis of enzyme-constrained metabolic models.
• These developments enable routine use of enzyme constraints for more accurate metabolic modeling and engineering.
• This work paves the way for broader application of enzyme-constrained models in systems biology.