Convergence in parameters and predictions using computational experimental design.
Convergence in parameters and predictions using computational experimental design.
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DOI：
10.1098/rsfs.2013.0008
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发表时间：
2013-08-06
期刊：
影响因子：
4.4
通讯作者：
Tidor B
中科院分区：
文献类型：
--
作者：
Hagen DR;White JK;Tidor B
通常，拟合实验数据的生物模型遭受显著的参数不确定性，这可能导致不准确或不确定的预测。一种思想流派认为，准确估计生物系统的真实参数本身就存在问题。然而，最近的工作表明，最优实验设计技术可以选择一组实验，其成员探测生化网络的互补方面，这些方面可以共同解释其全部行为。在这里，我们实现了一个实验设计方法，用于选择约束参数不确定性的实验集。我们用表皮生长因子-神经生长因子通路的模型证明，在综合地进行了一些最佳实验后，所有48个参数的不确定性收敛到10%以下。此外，拟合的参数收敛到它们的真实值，误差与残余不确定性一致。当未测试的实验条件与拟合模型进行模拟，预测的物种浓度收敛到其真实值的误差是一致的剩余不确定性。本文表明，通过专门为该任务设计的补充实验可以实现准确的参数估计，并且由此产生的参数化模型能够进行准确的预测。
Typically, biological models fitted to experimental data suffer from significant parameter uncertainty, which can lead to inaccurate or uncertain predictions. One school of thought holds that accurate estimation of the true parameters of a biological system is inherently problematic. Recent work, however, suggests that optimal experimental design techniques can select sets of experiments whose members probe complementary aspects of a biochemical network that together can account for its full behaviour. Here, we implemented an experimental design approach for selecting sets of experiments that constrain parameter uncertainty. We demonstrated with a model of the epidermal growth factor–nerve growth factor pathway that, after synthetically performing a handful of optimal experiments, the uncertainty in all 48 parameters converged below 10 per cent. Furthermore, the fitted parameters converged to their true values with a small error consistent with the residual uncertainty. When untested experimental conditions were simulated with the fitted models, the predicted species concentrations converged to their true values with errors that were consistent with the residual uncertainty. This paper suggests that accurate parameter estimation is achievable with complementary experiments specifically designed for the task, and that the resulting parametrized models are capable of accurate predictions.
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