High-dimensional Bayesian parameter estimation: Case study for a model of JAK2/STAT5 signaling
High-dimensional Bayesian parameter estimation: Case study for a model of JAK2/STAT5 signaling
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DOI：
10.1016/j.mbs.2013.04.002
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发表时间：
2013-12-01
影响因子：
4.3
通讯作者：
Theis, F. J.
中科院分区：
文献类型：
--
作者：
Hug, S.;Raue, A.;Theis, F. J.
在这项工作中，我们提出了一个详细的贝叶斯参数估计常微分方程模型的分析结果。这些取决于许多未知的参数，必须从实验数据中推断出来。然而，在高维参数空间中的统计推断在概念上和计算上是具有挑战性的。为了确保严格评估模型和预测的不确定性，我们同时利用了轮廓后验方法和马尔可夫链Monte Carlo sampling.我们分析了JAK 2/STAT 5信号转导通路的动力学模型，该模型包含100多个参数。使用轮廓后验，我们发现相应的后验分布是双峰的。为了保证在多峰后验分布的存在下有效混合，我们应用了多链抽样方法。贝叶斯参数估计，使评估的预测不确定性和设计额外的实验，提高了解释力的model.This研究的原理证明，详细的统计分析用于系统生物学中的定量动力学建模也是可行的，在高维参数空间。(C)2013 Elsevier Inc. All rights reserved.
In this work we present results of a detailed Bayesian parameter estimation for an analysis of ordinary differential equation models. These depend on many unknown parameters that have to be inferred from experimental data. The statistical inference in a high-dimensional parameter space is however conceptually and computationally challenging. To ensure rigorous assessment of model and prediction uncertainties we take advantage of both a profile posterior approach and Markov chain Monte Carlo sampling.We analyzed a dynamical model of the JAK2/STAT5 signal transduction pathway that contains more than one hundred parameters. Using the profile posterior we found that the corresponding posterior distribution is bimodal. To guarantee efficient mixing in the presence of multimodal posterior distributions we applied a multi-chain sampling approach. The Bayesian parameter estimation enables the assessment of prediction uncertainties and the design of additional experiments that enhance the explanatory power of the model.This study represents a proof of principle that detailed statistical analysis for quantitative dynamical modeling used in systems biology is feasible also in high-dimensional parameter spaces. (C) 2013 Elsevier Inc. All rights reserved.