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

Yoshiya Matsubara, Shinichi Kikuchi, Masahiro Sugimoto, Masaru Tomita

研究成果: Article査読

15 被引用数 (Scopus)


Background: The modeling of dynamic systems requires estimating kinetic parameters from experimentally measured time-courses. Conventional global optimization methods used for parameter estimation, e.g. genetic algorithms (GA), consume enormous computational time because they require iterative numerical integrations for differential equations. When the target model is stiff, the computational time for reaching a solution increases further. Results: In an attempt to solve this problem, we explored a learning technique that uses radial basis function networks (RBFN) to achieve a parameter estimation for biochemical models. RBFN reduce the number of numerical integrations by replacing derivatives with slopes derived from the distribution of searching points. To introduce a slight search bias, we implemented additional data selection using a GA that searches data-sparse areas at low computational cost. In addition, we adopted logarithmic transformation that smoothes the fitness surface to obtain a solution simply. We conducted numerical experiments to validate our methods and compared the results with those obtained by GA. We found that the calculation time decreased by more than 50% and the convergence rate increased from 60% to 90%. Conclusion: In this work, our RBFN technique was effective for parameter optimization of stiff biochemical models.

ジャーナルBMC bioinformatics
出版ステータスPublished - 2006

ASJC Scopus subject areas

  • 構造生物学
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 応用数学


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