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

BMC Bioinformatics
Yoshiya MatsubaraMasaru Tomita

Abstract

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. 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%. In this work, our...Continue Reading

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Citations

Jan 17, 2014·Systems and Synthetic Biology·Fatma A AlazabiSusmit Suvas
Aug 24, 2006·Bioinformatics·Zhike Zi, Edda Klipp
May 13, 2009·BMC Bioinformatics·Chih-Lung KoFeng-Sheng Wang
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Apr 6, 2011·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Jianyong SunCharlie Hodgman
Jul 2, 2019·Frontiers in Genetics·Pavel LoskotLyudmila Mihaylova

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