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Multi-start Evolutionary Nonlinear OpTimizeR (MENOTR): A hybrid parameter optimization toolbox.

Zachariah M Ingram1, Nathaniel W Scull1, David S Schneider2

  • 1Department of Chemistry, University of Alabama at Birmingham, Birmingham, AL, USA.

Biophysical Chemistry
|October 11, 2021
PubMed
Summary
This summary is machine-generated.

Parameter optimization using non-linear least squares (NLLS) can be biased by initial guesses. MENOTR, a new toolbox, uses a hybrid genetic algorithm/NLLS approach to overcome this bias in biochemical models.

Keywords:
Data fittingKineticsOptimizationThermodynamics

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Data Analysis

Background:

  • Non-linear least squares (NLLS) is widely used for parameter optimization in biochemical kinetics and thermodynamics.
  • NLLS algorithms can be dependent on initial parameter guesses, potentially introducing user bias.
  • Existing optimization tools often fail to address this initial guess dependency.

Purpose of the Study:

  • To present MENOTR, a novel data analysis tool designed to overcome the initial guess dependence in parameter optimization.
  • To demonstrate the capabilities of MENOTR using published experimental data.
  • To provide a user-friendly toolbox for researchers with limited programming experience.

Main Methods:

  • Development of MENOTR, a toolbox employing a hybrid genetic algorithm (GA) and NLLS approach.
  • Application of MENOTR to optimize parameters in kinetic and thermodynamic biochemical models.
  • Case studies using published experimental data to validate the tool's performance.

Main Results:

  • MENOTR effectively overcomes the initial guess dependence inherent in NLLS optimization.
  • The hybrid GA/NLLS strategy in MENOTR leverages the strengths of both algorithms while mitigating their weaknesses.
  • Optimized parameters obtained using MENOTR provide greater confidence in describing experimental data.

Conclusions:

  • MENOTR offers a robust solution for initial guess-independent parameter optimization in biochemical modeling.
  • The toolbox's design facilitates easy integration with new models, broadening its accessibility.
  • MENOTR enhances the reliability of data analysis in kinetic and thermodynamic investigations.