Bayesian model selection validates a biokinetic model for zirconium processing in humans.
Bayesian model selection validates a biokinetic model for zirconium processing in humans.
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DOI：
10.1186/1752-0509-6-95
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发表时间：
2012-08-05
影响因子：
--
通讯作者：
Theis FJ
中科院分区：
文献类型：
--
作者：
Schmidl D;Hug S;Li WB;Greiter MB;Theis FJ
在辐射防护中，锆加工的生物动力学模型在剂量估算和对暴露于这种放射性物质的人类的进一步风险分析中至关重要。它们提供了有害影响的限制值，并为内部剂量测定、各种器官中放射性锆保留的预测以及回顾性剂量测定中的应用奠定了基础。多室模型是模拟锆加工的首选工具。虽然很容易解释，但确定确切的隔室结构和相互作用机制通常是令人生畏的。在观察多室动态的背景下，贝叶斯方法为模型推断和选择提供了有效的工具。我们是第一个应用马尔可夫链蒙特卡罗方法来计算贝叶斯因子的两个竞争模型的锆处理在人体摄入后的评价。基于人体血浆和尿液水平的体内测量，我们能够表明，最近发表的模型上级国际辐射防护委员会的标准模型。贝叶斯因子估计的数值稳定的热力学积分结合最近开发的Copula为基础的Metropolis-Hastings采样器。与标准模型相比，新模型预测骨中锆的增加较低。这就降低了受照射个人的有害剂量水平。此外，贝叶斯方法允许回顾性剂量评估，包括最初摄入锆的可信间隔，比以前可能的方式更可靠。这里提出的所有方法都很容易适用于系统生物学中的许多建模任务。
In radiation protection, biokinetic models for zirconium processing are of crucial importance in dose estimation and further risk analysis for humans exposed to this radioactive substance. They provide limiting values of detrimental effects and build the basis for applications in internal dosimetry, the prediction for radioactive zirconium retention in various organs as well as retrospective dosimetry. Multi-compartmental models are the tool of choice for simulating the processing of zirconium. Although easily interpretable, determining the exact compartment structure and interaction mechanisms is generally daunting. In the context of observing the dynamics of multiple compartments, Bayesian methods provide efficient tools for model inference and selection. We are the first to apply a Markov chain Monte Carlo approach to compute Bayes factors for the evaluation of two competing models for zirconium processing in the human body after ingestion. Based on in vivo measurements of human plasma and urine levels we were able to show that a recently published model is superior to the standard model of the International Commission on Radiological Protection. The Bayes factors were estimated by means of the numerically stable thermodynamic integration in combination with a recently developed copula-based Metropolis-Hastings sampler. In contrast to the standard model the novel model predicts lower accretion of zirconium in bones. This results in lower levels of noxious doses for exposed individuals. Moreover, the Bayesian approach allows for retrospective dose assessment, including credible intervals for the initially ingested zirconium, in a significantly more reliable fashion than previously possible. All methods presented here are readily applicable to many modeling tasks in systems biology.
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