Modeling T-cell activation using gene expression profiling and state-space models
Modeling T-cell activation using gene expression profiling and state-space models
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DOI：
10.1093/bioinformatics/bth093
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发表时间：
2004-06-12
期刊：
影响因子：
5.8
通讯作者：
Falciani, F
中科院分区：
文献类型：
--
作者：
Rangel, C;Angus, J;Falciani, F
动机：我们已经使用状态空间模型从高度复制的基因表达谱时间序列数据中逆向工程转录网络，这些数据是从一个完善的T细胞激活模型中获得的。状态空间模型是一类动态贝叶斯网络，它假设观测到的测量值依赖于一些根据马尔可夫动力学演化的隐藏状态变量。这些隐藏的变量可以捕获的影响，不能在一个基因表达谱实验中测量，例如，基因还没有被包括在微阵列中，调节蛋白的水平，信使RNA和蛋白质降解的影响，等。结果：Bootstrap置信区间开发的参数代表“基因-基因”的相互作用随着时间的推移。我们的模型代表了T细胞活化的动力学，并为合理和实验可验证的假设的发展提供了一种方法。
Motivation: We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc.Results: Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses.