University of Freiburg, Institute for Physics, 79104 Freiburg, Germany, Merrimack Pharmaceuticals Inc., 02139 Cambridge, MA, USA, Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers Science Park, SE-412 88 Göteborg, Sweden, Department of Information Engineering, University of Padova, 35131 Padova, Italy, BIOSS Centre for Biological Signalling Studies and Zentrum für Biosystemanalyse (ZBSA), University of Freiburg, 79104 Freiburg, Germany.
Bioinformatics. 2014 May 15;30(10):1440-8. doi: 10.1093/bioinformatics/btu006. Epub 2014 Jan 23.
Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of Systems Biology. The amount of experimental data that are used to build and calibrate these models is often limited. In this setting, the model parameters may not be uniquely determinable. Structural or a priori identifiability is a property of the system equations that indicates whether, in principle, the unknown model parameters can be determined from the available data.
We performed a case study using three current approaches for structural identifiability analysis for an application from cell biology. The approaches are conceptually different and are developed independently. The results of the three approaches are in agreement. We discuss strength and weaknesses of each of them and illustrate how they can be applied to real world problems.
For application of the approaches to further applications, code representations (DAISY, Mathematica and MATLAB) for benchmark model and data are provided on the authors webpage.
使用常微分方程对动力系统进行建模是系统生物学领域的一种常用方法。用于构建和校准这些模型的实验数据量通常是有限的。在这种情况下，模型参数可能无法唯一确定。结构或先验可识别性是系统方程的一种属性，表明从可用数据中是否可以原则上确定未知的模型参数。
我们使用三种当前的结构可识别性分析方法对来自细胞生物学的应用进行了案例研究。这些方法在概念上有所不同，并且是独立开发的。三种方法的结果是一致的。我们讨论了它们各自的优缺点，并说明了如何将它们应用于实际问题。
为了将这些方法应用于进一步的应用，作者在其网页上提供了用于基准模型和数据的代码表示形式（DAISY、Mathematica 和 MATLAB）。
