Barman Shohag, Kwon Yung-Keun
School of Electrical Engineering, University of Ulsan, Daehak-ro, Nam-gu, Ulsan, Republic of Korea.
PLoS One. 2017 Feb 8;12(2):e0171097. doi: 10.1371/journal.pone.0171097. eCollection 2017.
Inferring a gene regulatory network from time-series gene expression data in systems biology is a challenging problem. Many methods have been suggested, most of which have a scalability limitation due to the combinatorial cost of searching a regulatory set of genes. In addition, they have focused on the accurate inference of a network structure only. Therefore, there is a pressing need to develop a network inference method to search regulatory genes efficiently and to predict the network dynamics accurately.
In this study, we employed a Boolean network model with a restricted update rule scheme to capture coarse-grained dynamics, and propose a novel mutual information-based Boolean network inference (MIBNI) method. Given time-series gene expression data as an input, the method first identifies a set of initial regulatory genes using mutual information-based feature selection, and then improves the dynamics prediction accuracy by iteratively swapping a pair of genes between sets of the selected regulatory genes and the other genes. Through extensive simulations with artificial datasets, MIBNI showed consistently better performance than six well-known existing methods, REVEAL, Best-Fit, RelNet, CST, CLR, and BIBN in terms of both structural and dynamics prediction accuracy. We further tested the proposed method with two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network, and also observed better results using MIBNI compared to the six other methods.
Taken together, MIBNI is a promising tool for predicting both the structure and the dynamics of a gene regulatory network.
在系统生物学中，从时间序列基因表达数据推断基因调控网络是一个具有挑战性的问题。已经提出了许多方法，其中大多数由于搜索调控基因集的组合成本而存在可扩展性限制。此外，它们仅专注于网络结构的准确推断。因此，迫切需要开发一种网络推断方法，以有效地搜索调控基因并准确预测网络动态。
在本研究中，我们采用具有受限更新规则方案的布尔网络模型来捕获粗粒度动态，并提出了一种基于互信息的新型布尔网络推断（MIBNI）方法。以时间序列基因表达数据作为输入，该方法首先使用基于互信息的特征选择来识别一组初始调控基因，然后通过在所选调控基因集和其他基因集之间迭代交换一对基因来提高动态预测准确性。通过对人工数据集的广泛模拟，MIBNI在结构和动态预测准确性方面均始终表现出比六种著名的现有方法（REVEAL、Best-Fit、RelNet、CST、CLR和BIBN）更好的性能。我们进一步使用大肠杆菌基因调控网络和裂殖酵母细胞周期网络的两个真实基因表达数据集测试了所提出的方法，并且与其他六种方法相比，使用MIBNI也观察到了更好的结果。
综上所述，MIBNI是预测基因调控网络结构和动态的一种有前途的工具。
