Data-based identifiability analysis of non-linear dynamical models
Data-based identifiability analysis of non-linear dynamical models
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DOI：
10.1093/bioinformatics/btm382
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发表时间：
2007-10-01
期刊：
影响因子：
5.8
通讯作者：
Maiwald, T.
中科院分区：
文献类型：
--
作者：
Hengl, S.;Kreutz, C.;Maiwald, T.
动机：生物系统的数学建模正在成为研究细胞过程中复杂动态、非线性相互作用机制的标准方法。然而，模型可以包括不能明确确定的不可识别的参数。非可识别性体现在功能相关的参数，这是很难detect.Results：我们提出的方法，平均最佳变换，非参数的基于Bootstrap的算法可识别性测试，能够识别任意多个参数的线性和非线性关系，无论模型的大小或复杂性。这是使用最优变换进行的，使用交替条件期望算法（ACE）进行估计。不需要关于参数的基本关系的初始猜测或先验知识。独立的，因此可识别的参数也被确定。在我们的方法中，包括处理的数据的质量，即非线性模型拟合数据和估计的参数值进行调查的函数关系。我们在一个现实的动力学模型上验证了我们的方法，并证明了在检测和固定结构不可识别性后，估计参数值的变异性从81%降低到1%。
Motivation: Mathematical modelling of biological systems is becoming a standard approach to investigate complex dynamic, non-linear interaction mechanisms in cellular processes. However, models may comprise non-identifiable parameters which cannot be unambiguously determined. Non-identifiability manifests itself in functionally related parameters, which are difficult to detect.Results: We present the method of mean optimal transformations, a non-parametric bootstrap-based algorithm for identifiability testing, capable of identifying linear and non-linear relations of arbitrarily many parameters, regardless of model size or complexity. This is performed with use of optimal transformations, estimated using the alternating conditional expectation algorithm (ACE). An initial guess or prior knowledge concerning the underlying relation of the parameters is not required. Independent, and hence identifiable parameters are determined as well. The quality of data at disposal is included in our approach, i.e. the non-linear model is fitted to data and estimated parameter values are investigated with respect to functional relations. We exemplify our approach on a realistic dynamical model and demonstrate that the variability of estimated parameter values decreases from 81 to 1% after detection and fixation of structural non-identifiabilities.