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Parameter estimation for stiff equations of biosystems using radial basis function networks.

Yoshiya Matsubara1, Shinichi Kikuchi, Masahiro Sugimoto

  • 1Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, 252-8520, Japan. yoshiya@sfc.keio.ac.jp

BMC Bioinformatics
|April 29, 2006
PubMed
Summary

This study introduces a novel Radial Basis Function Network (RBFN) technique for faster kinetic parameter estimation in stiff biochemical models. The RBFN method significantly reduces computation time and improves convergence rates compared to traditional genetic algorithms (GA).

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Systems Biology

Background:

  • Dynamic system modeling necessitates kinetic parameter estimation from experimental time-course data.
  • Conventional global optimization methods, like genetic algorithms (GA), are computationally intensive due to iterative numerical integrations, especially for stiff models.

Purpose of the Study:

  • To develop an efficient parameter estimation technique for biochemical models.
  • To reduce the computational burden associated with parameter estimation in dynamic systems.

Main Methods:

  • Employed Radial Basis Function Networks (RBFN) to approximate derivatives, reducing numerical integrations.
  • Integrated a genetic algorithm (GA) for targeted data selection in sparse areas, introducing a slight search bias.

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  • Utilized logarithmic transformation to simplify the fitness surface for easier optimization.

    • Main Results:

    • The RBFN technique decreased calculation time by over 50% compared to GA.
    • Achieved an improved convergence rate, increasing from 60% to 90% with the new method.
    • Demonstrated the effectiveness of RBFN for parameter optimization in stiff biochemical models.

    Conclusions:

    • The proposed RBFN technique offers a computationally efficient and effective solution for parameter optimization in stiff biochemical models.
    • This approach accelerates the analysis of dynamic systems and enhances the reliability of kinetic parameter estimation.