Matsubara Yoshiya, Kikuchi Shinichi, Sugimoto Masahiro, Tomita Masaru
Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, 252-8520, Japan.
BMC Bioinformatics. 2006 Apr 27;7:230. doi: 10.1186/1471-2105-7-230.
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 RBFN technique was effective for parameter optimization of stiff biochemical models.
动态系统建模需要根据实验测量的时间进程来估计动力学参数。用于参数估计的传统全局优化方法，如遗传算法（GA），由于需要对微分方程进行迭代数值积分，会消耗大量计算时间。当目标模型是刚性的时候，求解所需的计算时间会进一步增加。
为了解决这个问题，我们探索了一种学习技术，该技术使用径向基函数网络（RBFN）来实现生化模型的参数估计。RBFN通过用从搜索点分布导出的斜率代替导数来减少数值积分的次数。为了引入轻微的搜索偏差，我们使用GA实施了额外的数据选择，GA以低计算成本搜索数据稀疏区域。此外，我们采用对数变换来平滑适应度曲面以简单地获得一个解。我们进行了数值实验来验证我们的方法，并将结果与通过GA获得的结果进行比较。我们发现计算时间减少了50%以上，收敛率从60%提高到了90%。
在这项工作中，我们的RBFN技术对于刚性生化模型的参数优化是有效的。
