Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood
Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood
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DOI：
10.1093/bioinformatics/btp358
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发表时间：
2009-08-01
期刊：
影响因子：
5.8
通讯作者：
Timmer, J.
中科院分区：
文献类型：
--
作者：
Raue, A.;Kreutz, C.;Timmer, J.
动机：生物反应网络的微分方程的数学描述导致大的模型，其参数进行校准，以最佳地解释实验数据。通常只有部分模型可以直接观察到。给定一个充分描述测量数据的模型，重要的是推断模型参数由实验数据的数量和质量确定的程度。这些知识对于进一步研究模型预测是必不可少的。出于这个原因，在建模的一个主要议题是identifiability analysis.Results：我们提出了一种方法，利用亲。可能性。它能够检测结构不可识别性，这体现在功能相关的模型参数。此外，检测到实际的不可识别性，这可能是由于实验数据的数量和质量有限。最后但并非最不重要的是，可以导出置信区间。结果易于解释，可用于实验规划和模型简化。
Motivation: Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis.Results: We suggest an approach that exploits the pro. le likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction.