Bioprocess Engineering Group, IIM-CSIC, 36208 Vigo, Spain.
Bioinformatics. 2011 Aug 15;27(16):2311-3. doi: 10.1093/bioinformatics/btr370. Epub 2011 Jun 17.
Mathematical models of complex biological systems usually consist of sets of differential equations which depend on several parameters which are not accessible to experimentation. These parameters must be estimated by fitting the model to experimental data. This estimation problem is very challenging due to the non-linear character of the dynamics, the large number of parameters and the frequently poor information content of the experimental data (poor practical identifiability). The design of optimal (more informative) experiments is an associated problem of the highest interest.
This work presents AMIGO, a toolbox which facilitates parametric identification by means of advanced numerical techniques which cover the full iterative identification procedure putting especial emphasis on robust methods for parameter estimation and practical identifiability analyses, plus flexible capabilities for optimal experimental design.
The toolbox and the corresponding documentation may be downloaded from: http://www.iim.csic.es/~amigo
复杂生物系统的数学模型通常由一组微分方程组成，这些方程依赖于几个无法通过实验获得的参数。这些参数必须通过将模型拟合到实验数据来估计。由于动力学的非线性特征、参数数量大以及实验数据的信息含量通常较差（实际可识别性差），因此这个估计问题极具挑战性。设计最佳（更具信息量）的实验是一个非常关注的相关问题。
这项工作介绍了 AMIGO，这是一个工具箱，通过先进的数值技术来促进参数识别，这些技术涵盖了完整的迭代识别过程，特别强调了用于参数估计和实际可识别性分析的稳健方法，以及用于最佳实验设计的灵活功能。
可以从以下网址下载工具箱和相应的文档：http://www.iim.csic.es/~amigo
