ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models.

Area of Science:

• Metabolic Engineering
• Computational Biology
• Systems Biology

Background:

• Genome-scale metabolic models (GEMs) predict microorganism behavior using stoichiometric constraints.
• Standard GEMs show limitations in accurately reflecting experimental growth and yield, especially with rising substrate uptake.
• Enzyme-constrained models (ecMs) were developed to address GEM limitations by incorporating enzyme capacity, improving predictions and chemical production.

Purpose of the Study:

• To enhance the ECMpy toolbox for automated generation of enzyme-constrained genome-scale metabolic models (ecGEMs).
• To broaden the scope of ecGEM generation to a wider range of organisms.
• To improve user accessibility and utility of ecModel analysis and metabolic engineering applications.

Main Methods:

• Developed ECMpy 2.0, a Python-based workflow for automated ecGEM construction.
• Integrated automated retrieval of enzyme kinetic parameters and machine learning for parameter prediction.
• Incorporated common analytical and visualization features for ecModels.
• Integrated algorithms for identifying metabolic engineering targets using ecModels.

Main Results:

• ECMpy 2.0 automates ecGEM generation, significantly increasing parameter coverage through machine learning.
• Enhanced usability with integrated analysis and visualization tools for ecModels.
• Facilitated metabolic engineering by integrating target identification algorithms.

Conclusions:

• ECMpy 2.0 provides a user-friendly and automated solution for generating and analyzing ecGEMs.
• The enhanced toolbox supports broader applications in microbial biotechnology and metabolic engineering.
• Automated parameter prediction and integrated tools improve the efficiency and scope of ecModel-based research.