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Relative sensitivity analysis of the predictive properties of sloppy models

机译：邋型模型的预测性质的相对敏感性分析

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摘要

Commonly among the model parameters characterizing complex biological systems are those that do not significantly influence the quality of the fit to experimental data, so-called "sloppy" parameters. The sloppiness can be mathematically expressed through saturating response functions (Hill's, sigmoid) thereby embodying biological mechanisms responsible for the system robustness to external perturbations. However, if a sloppy model is used for the prediction of the system behavior at the altered input (e.g. knock out mutations, natural expression variability), it may demonstrate the poor predictive power due to the ambiguity in the parameter estimates. We introduce a method of the predictive power evaluation under the parameter estimation uncertainty, Relative Sensitivity Analysis. The prediction problem is addressed in the context of gene circuit models describing the dynamics of segmentation gene expression in Drosophila embryo. Gene regulation in these models is introduced by a saturating sigmoid function of the concentrations of the regulatory gene products. We show how our approach can be applied to characterize the essential difference between the sensitivity properties of robust and non-robust solutions and select among the existing solutions those providing the correct system behavior at any reasonable input. In general, the method allows to uncover the sources of incorrect predictions and proposes the way to overcome the estimation uncertainties.
机译：通常在表征复杂生物系统的模型参数中，是那些不会显着影响适合于实验数据的质量，所谓的“邋”的参数。通过饱和响应函数（Hill's，Sigmoid）可以数学方式表达邋，从而体现了负责系统鲁棒性对外部扰动的生物机制。然而，如果使用诽谤模型用于在改变的输入处预测系统行为（例如敲除突变，自然表达变异性），则它可以证明由于参数估计中的模糊而导致的预测力差。我们在参数估计不确定性，相对灵敏度分析下介绍了一种预测功率评估的方法。在描述果蝇胚胎中分段基因表达动态的基因电路模型的背景下解决了预测问题。这些模型中的基因调节由调节基因产物的浓度的饱和六样蛋白函数引入。我们展示了我们的方法如何应用于稳健和非强大解决方案的敏感性特性之间的本质区别，并在任何合理输入中提供正确的系统行为的现有解决方案中的选择。通常，该方法允许揭示不正确的预测来源，并提出克服估计不确定性的方法。

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