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. 2010 Aug 4;15(8):5354-68.
doi: 10.3390/molecules15085354.

Effects of time point measurement on the reconstruction of gene regulatory networks

Wenying Yan  1 , Huangqiong Zhu, Yang Yang, Jiajia Chen, Yuanyuan Zhang, Bairong Shen
Affiliations

Effects of time point measurement on the reconstruction of gene regulatory networks

Wenying Yan et al. Molecules. .

Abstract

With the availability of high-throughput gene expression data in the post-genomic era, reconstruction of gene regulatory networks has become a hot topic. Regulatory networks have been intensively studied over the last decade and many software tools are currently available. However, the impact of time point selection on network reconstruction is often underestimated. In this paper we apply the Dynamic Bayesian network (DBN) to construct the Arabidopsis gene regulatory networks by analyzing the time-series gene microarray data. In order to evaluate the impact of time point measurement on network reconstruction, we deleted time points one by one to yield 11 distinct groups of incomplete time series. Then the gene regulatory networks constructed based on complete and incomplete data series are compared in terms of statistics at different levels. Two time points are found to play a significant role in the Arabidopsis gene regulatory networks. Pathway analysis of significant nodes revealed three key regulatory genes. In addition, important regulations between genes, which were insensitive to the time point measurement, were also identified.

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Figures

Figure 1
Figure 1
The directed network of Arabidopsis gene regulation. Red nodes represent genes and arcs represent the regulation between genes.
Figure 2
Figure 2
The degree of nodes in the Arabidopsis gene regulatory network. The three pie charts A, B and C denote outdegree, indegree, and total degree separately.
Figure 3
Figure 3
The degree logarithmic distribution for 12 networks (G0-G11). Most of them fit power-law distribution well.
Figure 4
Figure 4
A is sensitivity of 11 time point removing networks with G0 as the standard network. B is precision of 11 networks and C shows F-measure.
Figure 5
Figure 5
The degree logarithmic distribution for G2_3 and G9_10.
Figure 6
Figure 6
A is sensitivity of G2, G3, G9, G10, G2_3 and G9_10 with G0 as the standard network. B is precision of these 6 networks and C shows F-measure.
Figure 7
Figure 7
The number of overlapping edges in different networks.
See this image and copyright information in PMC

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