Maximin and Bayesian robust experimental design for measurement set selection in modelling biochemical regulatory systems
Maximin and Bayesian robust experimental design for measurement set selection in modelling biochemical regulatory systems
复制标题
用于生化调控系统建模中测量集选择的 Maximin 和贝叶斯稳健实验设计
DOI：
10.1002/rnc.1558
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发表时间：
2010-06-01
影响因子：
3.9
通讯作者：
Yue, Hong
中科院分区：
文献类型：
--
作者：
He, Fei;Brown, Martin;Yue, Hong
实验设计在系统识别中很重要，特别是当模型复杂且测量数据稀疏且有噪声时，这在生化调控网络建模中经常发生。传统的最优试验设计的质量很大程度上取决于模型参数估计的准确性，而在设计阶段，模型参数估计往往是不可得的或估计得很差。因此，稳健的实验设计（RED）算法已被提出时，模型参数的不确定性，需要在设计过程中加以解决。本文研究了两种鲁棒设计策略，并对信号通路模型进行了比较研究。第一种方法是最大最小实验设计方法，这是一种最坏情况下的设计策略，第二种方法是贝叶斯实验设计，“取平均值”的参数不确定性的影响。用局部泰勒表示描述结构不确定性的极大极小设计的局限性进行了定量评估。为了更好地定量评估最大极小和贝叶斯RED之间的差异，提出了有效设计参数的概念，特别是在大的模型不确定性的情况下，贝叶斯设计的优势被证明。版权所有（C）2010约翰威利父子有限公司
Experimental design is important in system identification, especially when the models are complex and the measurement data are sparse and noisy, as often occurs in modelling of biochemical regulatory networks. The quality of conventional optimal experimental design largely depends on the accuracy of model parameter estimation, which is often either unavailable or poorly estimated at the stage of design. Robust experimental design (RED) algorithms have thus been proposed when model parametric uncertainties need to be addressed during the design process. In this paper, two robust design strategies are investigated and the comparative study has been made on signal pathway models. The first method is a maximin experimental design approach which is a worst-case design strategy, and the second method is the Bayesian experimental design that 'takes an average' of the parametric uncertainty effects. The limitations of the maximin design which describes the structural uncertainty using a local Taylor representation are quantitatively evaluated. To better quantitatively assess the differences between the maximin and the Bayesian REDs, a concept of effective design parameters is proposed, from which the advantages of the Bayesian design is demonstrated especially in the case of large model uncertainties. Copyright (C) 2010 John Wiley & Sons, Ltd.