ECMpy 2.0

ECMpy 2.0 automates the construction and analysis of enzyme-constrained models (ecModels) for microorganisms, integrating algorithms for metabolic engineering target discovery.

Key Features:

• Automated ecModel construction: Directly imposes enzyme amount constraints in genome-scale metabolic models and accounts for protein subunit composition in reactions.
• Automated parameter retrieval: Automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters, significantly enhancing parameter coverage.
• Analytical and visualization features: Introduces common analytical and visualization features for ecModels.
• Metabolic engineering target discovery: Seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering.

Scientific Applications:

• Enzyme-constrained model construction: Generates enzyme-constrained genome-scale metabolic models (ecGEMs) for a wide array of organisms.
• Metabolic engineering: Identifies potential targets for metabolic engineering to enhance chemical production in microorganisms.
• Growth and yield prediction: Overcomes the limitation of traditional GEMs where simulated growth and product yields linearly increase with substrate uptake rates, diverging from experimental measurements.

Methodology:

ECMpy 2.0 uses a Python-based workflow that directly imposes enzyme amount constraints in genome-scale metabolic models and accounts for protein subunit composition in reactions; automates retrieval of enzyme kinetic parameters; employs machine learning for predicting enzyme kinetic parameters; and integrates three published algorithms for metabolic engineering target discovery.