Reconstruction of Dynamic Gene Regulatory Networks for Cell Differentiation by Separation of Time-course Data
Reconstruction of Dynamic Gene Regulatory Networks for Cell Differentiation by Separation of Time-course Data
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发表时间：
2013
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通讯作者：
T. Nakayama;H. Daiyasu;S. Seno;Y. Takenaka;H. Matsuda
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作者：
T. Nakayama;H. Daiyasu;S. Seno;Y. Takenaka;H. Matsuda
近年来，动态贝叶斯网络（DBN）模型被广泛应用于从基因表达的时程数据中估计基因调控网络（GRNs）。普通的动态贝叶斯网络只使用整个时间过程的数据来估计单个网络。然而，一些GRN，如细胞分化，由于染色质重塑而动态改变其网络结构。在本文中，我们提出了一种方法来估计这样的动态GRNs，遵循动态变化的规定，在脂肪细胞分化分离的时间过程中的数据。我们分析了估计的GRNs，并证实GRNs显示了脂肪细胞调节的动态变化。结果表明，我们的方法可以通过分离时程数据来识别脂肪细胞分化过程中动态变化的基因的调控关系。
Recently, dynamic Bayesian network (DBN) model is widely used for estimating gene regulatory networks (GRNs) from time-course gene expression data. Ordinary DBNs estimate only a single network using the whole timecourse data. However, some GRNs, such as cell differentiation, dynamically change their network structures due to chromatin remodeling. In this papers we present a method to estimate such dynamic GRNs that follow the dynamic changes of the regulations in adipocyte differentiation by separating time-course data. We analyzed the estimated GRNs and confirmed that the GRNs showed the dynamic changes in adipocyte regulation. The result shows that our method can identify the regulatory relationships of the genes that are dynamically changing during adipocyte differentiation by separating the time-course data.