A Bayesian approach to reconstructing genetic regulatory networks with hidden factors
A Bayesian approach to reconstructing genetic regulatory networks with hidden factors
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DOI：
10.1093/bioinformatics/bti014
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发表时间：
2005-02-01
期刊：
影响因子：
5.8
通讯作者：
Wild, DL
中科院分区：
文献类型：
--
作者：
Beal, MJ;Falciani, F;Wild, DL
动机：我们已经使用状态空间模型（SSM）从高度复制的基因表达谱时间序列数据的T细胞活化的一个完善的模型获得的反向工程转录网络。SSM是一类动态贝叶斯网络，其中观察到的测量值取决于一些隐状态变量，根据马尔可夫动力学演变。这些隐藏的变量可以捕获的影响，不能直接测量的基因表达谱实验中，例如：基因，还没有被包括在微阵列中，调节蛋白的水平，mRNA和蛋白质降解的影响，等。结果：我们已经接近的问题，推断这些状态空间模型的模型结构，使用经典和贝叶斯方法。在我们以前的工作中，一个bootstrap程序被用来获得经典的置信区间的参数代表“基因-基因”的相互作用随着时间的推移。在这篇文章中，变分近似被用来执行类似的模型选择任务的贝叶斯上下文中。在这些监管网络的经典和贝叶斯分析中存在某些相互作用。由此产生的模型将JunB和JunD置于控制细胞凋亡和增殖的机制的中心。这些机制是克隆扩增和控制这些细胞的长期行为（例如程序性细胞死亡）的关键。
Motivation: We have used state-space models (SSMs) to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation. SSMs are a class of dynamic Bayesian networks in which the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be directly measured in a gene expression profiling experiment, for example: genes that have not been included in the microarray, levels of regulatory proteins, the effects of mRNA and protein degradation, etc.Results: We have approached the problem of inferring the model structure of these state-space models using both classical and Bayesian methods. In our previous work, a bootstrap procedure was used to derive classical confidence intervals for parameters representing 'gene-gene' interactions over time. In this article, variational approximations are used to perform the analogous model selection task in the Bayesian context. Certain interactions are present in both the classical and the Bayesian analyses of these regulatory networks. The resulting models place JunB and JunD at the centre of the mechanisms that control apoptosis and proliferation. These mechanisms are key for clonal expansion and for controlling the long term behavior (e.g. programmed cell death) of these cells.