在动物科学中,什么是好的(有用的)数学模型?对于为预测目的而构建的模型,传统上,模型充分性(有用性)的问题是通过将统计分析应用于观察到的实验数据相对于模型预测变量来解决的。然而,很少注意利用模型方程的数学特性的分析工具。例如,在模型校准的背景下,在尝试对模型参数进行数值估计之前,我们可能想知道,我们是否有机会成功地从可用的测量中估计出模型参数的唯一最佳值。这种唯一性问题被称为结构可识别性;一种数学属性,是在假设的理想实验中,仅基于模型结构定义的,该实验由模型输入(刺激)和可观察变量(测量)的设置确定。应用于常微分方程(ODE)描述的动态模型的结构可识别性分析是控制工程和系统识别中的常见做法。这种分析需要超出动物科学学术背景的数学技术,这可能解释了动物科学建模中缺乏可识别性分析的普遍性。为了填补这个空白,在本文中,我们通过利用专用软件工具的使用,从从业者的角度对结构可识别性进行了分析。我们的目标是(i)为动物科学建模社区提供结构可识别性概念的全面解释,(ii)评估动物科学建模中可识别性分析的相关性,以及(iii)激励社区在建模实践中使用可识别性分析(当可识别性问题相关时)。我们的研究重点是ODE模型。通过使用说明性的例子,包括已发表的描述牛泌乳的数学模型,我们展示了结构可识别性分析如何有助于推进动物科学中的数学建模,以产生有用的模型,此外,通过优化实验设计进行信息丰富的实验。而不是试图在模型开发过程中对建模社区进行系统的可识别性分析,我们希望为发现模型构建和实验设计的强大工具打开一个窗口。
What is a good (useful) mathematical model in animal science? For models constructed for prediction purposes, the question of model adequacy (usefulness) has been traditionally tackled by statistical analysis applied to observed experimental data relative to model-predicted variables. However, little attention has been paid to analytic tools that exploit the mathematical properties of the model equations. For example, in the context of model calibration, before attempting a numerical estimation of the model parameters, we might want to know if we have any chance of success in estimating a unique best value of the model parameters from available measurements. This question of uniqueness is referred to as structural identifiability; a mathematical property that is defined on the sole basis of the model structure within a hypothetical ideal experiment determined by a setting of model inputs (stimuli) and observable variables (measurements). Structural identifiability analysis applied to dynamic models described by ordinary differential equations (ODEs) is a common practice in control engineering and system identification. This analysis demands mathematical technicalities that are beyond the academic background of animal science, which might explain the lack of pervasiveness of identifiability analysis in animal science modelling. To fill this gap, in this paper we address the analysis of structural identifiability from a practitioner perspective by capitalizing on the use of dedicated software tools. Our objectives are (i) to provide a comprehensive explanation of the structural identifiability notion for the community of animal science modelling, (ii) to assess the relevance of identifiability analysis in animal science modelling and (iii) to motivate the community to use identifiability analysis in the modelling practice (when the identifiability question is relevant). We focus our study on ODE models. By using illustrative examples that include published mathematical models describing lactation in cattle, we show how structural identifiability analysis can contribute to advancing mathematical modelling in animal science towards the production of useful models and, moreover, highly informative experiments via optimal experiment design. Rather than attempting to impose a systematic identifiability analysis to the modelling community during model developments, we wish to open a window towards the discovery of a powerful tool for model construction and experiment design.