Cause and cure of sloppiness in ordinary differential equation models.
Cause and cure of sloppiness in ordinary differential equation models.
复制标题
常微分方程模型中草率的原因和解决方法。
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
C. Kreutz
中科院分区：
文献类型：
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作者：
C. Tönsing;J. Timmer;C. Kreutz
基于数据的生化反应网络数学建模，例如非线性常微分方程（ODE）模型，已得到成功应用。在这种情况下，参数估计和不确定性分析是评估模型描述系统的质量的一项主要任务。最近，观察到目标函数的 Hessian 矩阵的特征值谱扩大了几个数量级，并被称为“草率”。在这项工作中，我们研究了模型拓扑和实验设计的特性引起的灵敏度矩阵结构的草率根源。此外，我们提出了使用最佳实验设计方法的策略，以避免草率问题，并为基准模型提供非草率设计。
Data-based mathematical modeling of biochemical reaction networks, e.g., by nonlinear ordinary differential equation (ODE) models, has been successfully applied. In this context, parameter estimation and uncertainty analysis is a major task in order to assess the quality of the description of the system by the model. Recently, a broadened eigenvalue spectrum of the Hessian matrix of the objective function covering orders of magnitudes was observed and has been termed as sloppiness. In this work, we investigate the origin of sloppiness from structures in the sensitivity matrix arising from the properties of the model topology and the experimental design. Furthermore, we present strategies using optimal experimental design methods in order to circumvent the sloppiness issue and present nonsloppy designs for a benchmark model.