MLAGO

MLAGO integrates machine learning with constrained global optimization to improve estimation of the Michaelis constant (K_m) for kinetic modeling.

Key Features:

• Machine learning predictor: Estimates K_m using EC number, KEGG Compound ID, and Organism ID.
• Predictor performance: Achieves RMSE 0.795 and R² 0.536 for K_m prediction.
• Constrained global optimization: Uses machine learning–predicted K_m values as constraints during global optimization.
• Reduced computational cost: Constraining optimization with ML predictions lowers computational demands compared to traditional global optimization.
• Improved data agreement: Reduces errors between simulated and experimental data by aligning estimated K_m with measurements.
• Non-identifiability mitigation: Addresses non-identifiability where multiple parameter sets fit the data equally well, producing realistic K_m estimates that closely match measured values.

Scientific Applications:

• K_m estimation in kinetic modeling: Provides reference and optimized K_m values for parameterizing kinetic models.
• Parameter identifiability: Mitigates non-identifiability in kinetic parameter inference.
• Simulation–experiment alignment: Improves agreement between model simulations and experimental data.
• Accelerating kinetic modeling: Speeds up kinetic modeling and analysis of complex cellular systems.

Methodology:

Use a machine learning predictor to estimate K_m from EC number, KEGG Compound ID, and Organism ID (RMSE 0.795, R² 0.536), then perform constrained global optimization using the predicted K_m values as constraints.