论文标题
修复动态模型：一种获得具有机械见解的可识别且可观察到的重新聚集的方法
Repairing dynamic models: a method to obtain identifiable and observable reparameterizations with mechanistic insights
论文作者
论文摘要
机械动力学模型允许对数据进行定量和系统的解释以及可检验的假设的产生。但是，这些模型通常被过度参数化，导致非识别性和不可遵守性，即不可能推断其参数和状态变量。缺乏结构性可识别性和可观察性（SIO）损害了模型做出预测和提供见解的能力。在这里，我们提出了一种方法，即自动核心，该方法会自动纠正SIO缺陷，从而产生重新聚集的模型，这些模型在结构上可识别且可观察到。重新聚集化保留了所选变量的机械含义，并且具有与原始模型完全相同的动力学和输入输出映射。我们实施自动Par，作为用于SIO分析的Strike-Goldd软件工具箱的扩展，将其应用于文献中的几种模型，以证明其修复其结构缺陷的能力。 Autorepar提高了机械模型的适用性，从而使它们能够提供有关其参数和动态的可靠信息。
Mechanistic dynamic models allow for a quantitative and systematic interpretation of data and the generation of testable hypotheses. However, these models are often over-parameterized, leading to non-identifiability and non-observability, i.e. the impossibility of inferring their parameters and state variables. The lack of structural identifiability and observability (SIO) compromises a model's ability to make predictions and provide insight. Here we present a methodology, AutoRepar, that corrects SIO deficiencies automatically, yielding reparameterized models that are structurally identifiable and observable. The reparameterization preserves the mechanistic meaning of selected variables, and has the exact same dynamics and input-output mapping as the original model. We implement AutoRepar as an extension of the STRIKE-GOLDD software toolbox for SIO analysis, applying it to several models from the literature to demonstrate its ability to repair their structural deficiencies. AutoRepar increases the applicability of mechanistic models, enabling them to provide reliable information about their parameters and dynamics.
