Sloppy models, parameter uncertainty, and the role of experimental design.

Sloppy models, parameter uncertainty, and the role of experimental design.
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DOI:
10.1039/b918098b
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发表时间:
2010-10
影响因子:
--
通讯作者:
Tidor B
Tidor B
中科院分区:
生物3区
文献类型:
--
作者:
Apgar JF;Witmer DK;White FM;Tidor B

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计算模型越来越多地被用来理解和预测复杂的生物现象。这些模型包含许多未知参数,其中至少有一些很难直接测量,而是通过对时间进程数据的拟合来估计的。以前的工作表明,即使有精确的数据集,许多参数通过弹道测量也是未知的。我们在表皮生长因子(EGF)和神经生长因子(NGF)信号通路模型的背景下研究了这个问题。在计算上,我们检查了一系列实验扰动,包括不同剂量的EGF和NGF,以及单基因和多基因敲除和过度表达。虽然没有一个单一的实验可以准确地估计所有的参数,但实验设计方法学确定了一套可以做到的五个互补实验。这些结果表明,对校准即使是大型模型的前景也持乐观态度,参数估计的成功与所使用的实验扰动密切相关,实验设计方法对于生物模型的参数拟合以及可能达到的预期精度是重要的。
Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by trajectory measurements. We examined this question in the context of a pathway model of epidermal growth factor (EGF) and neuronal growth factor (NGF) signaling. Computationally, we examined a palette of experimental perturbations that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, experimental design methodology identified a set of five complementary experiments that could. These results suggest optimism for the prospects for calibrating even large models, that the success of parameter estimation is intimately linked to the experimental perturbations used, and that experimental design methodology is important for parameter fitting of biological models and likely for the accuracy that can be expected for them.
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