Bayesian Emulation and Calibration of a Stochastic Computer Model of Mitochondrial DNA Deletions in Substantia Nigra Neurons
Bayesian Emulation and Calibration of a Stochastic Computer Model of Mitochondrial DNA Deletions in Substantia Nigra Neurons
复制标题
DOI：
10.1198/jasa.2009.0005
复制
发表时间：
2009-03-01
影响因子：
3.7
通讯作者：
Wilkinson, Darren J.
中科院分区：
文献类型：
--
作者：
Henderson, Daniel A.;Boys, Richard J.;Wilkinson, Darren J.
本文考虑了一个随机生物模型的参数估计问题的线粒体DNA群体动力学使用的实验数据的缺失突变积累。随机模型试图描述人脑黑质区域中缺失积累和神经元损失之间的假设联系。模型的参数的推断是复杂的，因为该模型在分析上是难以处理的，并且采样缓慢。我们展示了如何随机模型可以近似使用一个简单的参数统计模型与平滑变化的参数。这些参数被视为未知函数，并使用高斯过程先验建模。我们的贝叶斯模型的几个简化实现，以减轻计算负担。在本文中，我们使用预测模拟来验证我们的模型。我们证明了我们的拟合模型在黑质神经元存活的独立数据集上的有效性。
This article considers the problem of parameter estimation for a stochastic biological model of mitochondrial DNA population dynamics using experimental data on deletion mutation accumulation. The stochastic model is an attempt to describe the hypothesized link between deletion accumulation and neuronal loss in the substantia nigra region of the human brain. Inference for the parameters of the model is complicated by the fact that the model is both analytically intractable and slow to sample from. We show how the stochastic model can be approximated using a simple parametric statistical model with smoothly varying parameters. These parameters are treated as unknown functions and modeled using Gaussian process priors. Several simplifications of our Bayesian model are implemented to ease the computational burden. Throughout the article, we validate our models using predictive simulations. We demonstrate the validity of our fitted model on an independent dataset of substantia nigra neuron survival.