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

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...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Yoshiya Matsubara, Shinichi Kikuchi, Masahiro Sugimoto, Masaru Tomita
• Keywords: Biochemical Models, BMCBI 2006, Numerical Integration, Parameters