title: Parameter estimation for stiff equations of biosystems using radial basis function networks.
abstract: 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%.
  
  
  CONCLUSIONS
  In this work, our RBFN technique was effective for parameter optimization of stiff biochemical models.
authors.affiliation: Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, 252-8520, Japan. yoshiya@sfc.keio.ac.jp
authors.forename: Yoshiya
authors.initials: Y
authors.lastname: Matsubara
authors.forename: Shinichi
authors.initials: S
authors.lastname: Kikuchi
authors.forename: Masahiro
authors.initials: M
authors.lastname: Sugimoto
authors.forename: Masaru
authors.initials: M
authors.lastname: Tomita
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D007700:Kinetics; D008954:Models, Biological