MENOTR

MENOTR performs hybrid optimization to estimate parameters in nonlinear biochemical kinetic and thermodynamic models, reducing initial-guess dependence in non-linear least squares analyses.

Key Features:

• Hybrid Optimization Algorithm: Combines genetic algorithms (GA) with non-linear least squares (NLLS) to balance robustness to initial guesses and convergence speed.
• Multi-start and Evolutionary Strategies: Employs multiple starting points and GA evolutionary strategies to mitigate initial-guess dependence and explore parameter space.
• Handling of Correlated Parameters: Designed to optimize models with correlated parameters, reducing bias from sensitive initial guesses.
• Application to Experimental Kinetic/Thermodynamic Models: Applied to kinetic and thermodynamic biochemical models, including chemical-quenched flow, stopped-flow, and molecular tweezers datasets.
• Validation with Published Data: Case studies using published experimental data demonstrate mitigation of initial-guess dependence.

Scientific Applications:

• Kinetic Parameter Estimation: Estimation of rate constants and kinetic parameters from chemical-quenched flow and stopped-flow experiments.
• Thermodynamic Parameter Estimation: Estimation of thermodynamic parameters from biochemical models and molecular tweezers experiments.
• Robust Inference in Correlated Models: Providing increased confidence in optimized parameters for models with parameter correlations.

Methodology:

Hybrid optimization combining genetic algorithms (GA) with non-linear least squares (NLLS), using multiple starting points and GA evolutionary strategies to mitigate initial-guess dependence.