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使用 DAISY 软件测试生物和生物医学模型全局可识别性的示例。

Examples of testing global identifiability of biological and biomedical models with the DAISY software.

机构信息

Department of Information Engineering, University of Padova, Padova, Italy.

出版信息

Comput Biol Med. 2010 Apr;40(4):402-7. doi: 10.1016/j.compbiomed.2010.02.004. Epub 2010 Feb 24.

Abstract

DAISY (Differential Algebra for Identifiability of SYstems) is a recently developed computer algebra software tool which can be used to automatically check global identifiability of (linear and) nonlinear dynamic models described by differential equations involving polynomial or rational functions. Global identifiability is a fundamental prerequisite for model identification which is important not only for biological or medical systems but also for many physical and engineering systems derived from first principles. Lack of identifiability implies that the parameter estimation techniques may not fail but any obtained numerical estimates will be meaningless. The software does not require understanding of the underlying mathematical principles and can be used by researchers in applied fields with a minimum of mathematical background. We illustrate the DAISY software by checking the a priori global identifiability of two benchmark nonlinear models taken from the literature. The analysis of these two examples includes comparison with other methods and demonstrates how identifiability analysis is simplified by this tool. Thus we illustrate the identifiability analysis of other two examples, by including discussion of some specific aspects related to the role of observability and knowledge of initial conditions in testing identifiability and to the computational complexity of the software. The main focus of this paper is not on the description of the mathematical background of the algorithm, which has been presented elsewhere, but on illustrating its use and on some of its more interesting features. DAISY is available on the web site http://www.dei.unipd.it/ approximately pia/.

摘要

DAISY(系统可识别性的微分代数)是一种新开发的计算机代数软件工具,可用于自动检查涉及多项式或有理函数的微分方程描述的(线性和)非线性动态模型的全局可识别性。全局可识别性是模型识别的基本前提,这不仅对生物或医学系统很重要,而且对许多从第一原理推导出来的物理和工程系统也很重要。缺乏可识别性意味着参数估计技术可能不会失败,但任何获得的数值估计都将毫无意义。该软件不需要了解底层数学原理,并且具有最小数学背景的应用领域的研究人员都可以使用。我们通过检查来自文献的两个基准非线性模型的先验全局可识别性来说明 DAISY 软件。对这两个示例的分析包括与其他方法的比较,并演示了该工具如何简化可识别性分析。因此,我们通过讨论与可观察性和初始条件知识在测试可识别性中的作用以及软件的计算复杂性相关的一些特定方面,包括另外两个示例的可识别性分析。本文的主要重点不是描述算法的数学背景,该背景已在其他地方介绍过,而是说明其用途和一些更有趣的功能。DAISY 可在网站 http://www.dei.unipd.it/ 上获得。

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