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Statistical method for estimation of the predictive power of a gene circuit model

机译：估计基因电路模型预测能力的统计方法

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In this paper, a specific aspect of the prediction problem is considered: high predictive power is understood as a possibility to reproduce correct behavior of model solutions at predefined values of a subset of parameters. The problem is discussed in the context of a specific mathematical model, the gene circuit model for segmentation gap gene system in early Drosophila development. A shortcoming of the model is that it cannot be used for predicting the system behavior in mutants when fitted to wild type (WT) data. In order to answer a question whether experimental data contain enough information for the correct prediction we introduce two measures of predictive power. The first measure reveals the biologically substantiated low sensitivity of the model to parameters that are responsible for correct reconstruction of expression patterns in mutants, while the second one takes into account their correlation with the other parameters. It is demonstrated that the model solution, obtained by fitting to gene expression data in WT and Kr~- mutants simultaneously, and exhibiting the high predictive power, is characterized by much higher values of both measures than those fitted to WT data alone. This result leads us to the conclusion that information contained in WT data is insufficient to reliably estimate the large number of model parameters and provide predictions of mutants.
机译：在本文中，考虑了预测问题的特定方面：高预测能力被理解为在参数子集的预定义值下重现模型解的正确行为的可能性。在特定的数学模型的背景下讨论了该问题，该模型是果蝇早期发育中的分割缺口基因系统的基因电路模型。该模型的缺点是，当拟合到野生型（WT）数据时，该模型不能用于预测突变体中的系统行为。为了回答一个问题，实验数据是否包含足够的信息以进行正确的预测，我们引入了两种预测能力度量。第一种方法揭示了该模型对参数的生物学证实的低敏感性，这些参数负责正确重建突变体中的表达模式，而第二种方法则考虑了它们与其他参数的相关性。结果表明，通过同时拟合WT和Kr〜-突变体中的基因表达数据并显示出较高的预测能力而获得的模型解决方案，其特征在于两种方法的值均高于单独拟合WT数据的方法。该结果使我们得出结论：WT数据中包含的信息不足以可靠地估计大量模型参数并提供突变体的预测。

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