Gong Haijun, Klinger Jakob, Damazyn Kevin, Li Xiangrui, Huang Shiyang
BMC Bioinformatics. 2015;16 Suppl 7(Suppl 7):S7. doi: 10.1186/1471-2105-16-S7-S7. Epub 2015 Apr 23.
The reconstruction of gene regulatory network from time course microarray data can help us comprehensively understand the biological system and discover the pathogenesis of cancer and other diseases. But how to correctly and efficiently decifer the gene regulatory network from high-throughput gene expression data is a big challenge due to the relatively small amount of observations and curse of dimensionality. Computational biologists have developed many statistical inference and machine learning algorithms to analyze the microarray data. In the previous studies, the correctness of an inferred regulatory network is manually checked through comparing with public database or an existing model.
In this work, we present a novel procedure to automatically infer and verify gene regulatory networks from time series expression data. The dynamic Bayesian network, a statistical inference algorithm, is at first implemented to infer an optimal network from time series microarray data of S. cerevisiae, then, a weighted symbolic model checker is applied to automatically verify or falsify the inferred network through checking some desired temporal logic formulas abstracted from experiments or public database.
Our studies show that the marriage of statistical inference algorithm with model checking technique provides a more efficient way to automatically infer and verify the gene regulatory network from time series expression data than previous studies.
从时间序列微阵列数据重建基因调控网络有助于我们全面理解生物系统，并发现癌症和其他疾病的发病机制。然而，由于观测数据量相对较少以及维度灾难问题，如何从高通量基因表达数据中正确且高效地解读基因调控网络是一项巨大挑战。计算生物学家已开发出许多统计推断和机器学习算法来分析微阵列数据。在先前的研究中，通过与公共数据库或现有模型进行比较来人工检查推断出的调控网络的正确性。
在这项工作中，我们提出了一种从时间序列表达数据中自动推断和验证基因调控网络的新方法。首先使用一种统计推断算法——动态贝叶斯网络，从酿酒酵母的时间序列微阵列数据中推断出一个最优网络，然后，应用加权符号模型检查器，通过检查从实验或公共数据库中提取的一些所需时态逻辑公式，自动验证或证伪推断出的网络。
我们的研究表明，与先前的研究相比，统计推断算法与模型检查技术相结合为从时间序列表达数据中自动推断和验证基因调控网络提供了一种更有效的方法。
