Bayesian Model Comparison and Parameter Inference in Systems Biology Using Nested Sampling
Bayesian Model Comparison and Parameter Inference in Systems Biology Using Nested Sampling
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DOI：
10.1371/journal.pone.0088419
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发表时间：
2014-02-11
期刊：
影响因子：
3.7
通讯作者：
Morris, Richard J.
中科院分区：
文献类型：
--
作者：
Pullen, Nick;Morris, Richard J.
推断生物过程模型的参数是当前系统生物学中的一个挑战，比较解释数据的竞争模型也是一个相关问题。在这项工作中，我们应用斯基林的嵌套抽样来解决这两个问题。嵌套抽样是一种贝叶斯方法，用于探索参数空间，将多维积分转换为似然空间上的一维积分。该方法侧重于边际似然或证据的计算。不同模型的证据之比产生贝叶斯因子，贝叶斯因子可用于模型比较。我们演示了如何使用嵌套采样来逆向工程系统的行为，同时考虑到结果中的不确定性。研究了变量初始条件缺失和未知参数的影响。我们展示了证据和模型排名如何作为可用数据的函数而变化。此外，从系统的额外变量中增加数据比从一个变量中增加数据可以提供更多的信息用于模型比较，从而为实验设计提供依据。
Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focusses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design.