Department of BioMedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Bioinformatics. 2012 Apr 15;28(8):1136-42. doi: 10.1093/bioinformatics/bts092. Epub 2012 Feb 24.
Systems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. However, existing methods for optimal experiment design (OED) rely on assumptions that are inappropriate when data are scarce considering model complexity.
We have developed a novel method to perform OED for models that cope with large parameter uncertainty. We employ a Bayesian approach involving importance sampling of the posterior predictive distribution to predict the efficacy of a new measurement at reducing the uncertainty of a selected prediction. We demonstrate the method by applying it to a case where we show that specific combinations of experiments result in more precise predictions.
Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html.
系统生物学运用数学建模来增进我们对生化途径的理解。由于模型参数化所依据的实验数据量通常有限，这些模型在参数和预测方面都存在很大的不确定性。统计方法可用于选择以最佳方式减少这种不确定性的实验。然而，现有的最优实验设计 (OED) 方法依赖于在考虑模型复杂性时数据稀缺的不适当假设。
我们开发了一种新方法，用于对处理大参数不确定性的模型进行最优实验设计。我们采用贝叶斯方法，通过对后验预测分布进行重要性抽样，来预测新测量在减少选定预测的不确定性方面的效果。我们通过将其应用于一个案例来证明该方法，该案例表明，特定的实验组合可产生更精确的预测。
