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.
Highlights
Most biological networks, such as gene regulatory networks, protein-protein interaction networks and metabolic networks, are known to be complex and dynamic systems
To the best of our knowledge, until now, few works focused on the effect of time point measurements on the reconstruction of biological networks were reported
The gene regulatory networks based on Arabidopsis time series data were constructed, and the effect of the time point measurements on the network reconstruction was investigated
Summary
Most biological networks, such as gene regulatory networks, protein-protein interaction networks and metabolic networks, are known to be complex and dynamic systems. Many gene expression data in current microarray databases are static, which can hardly describe the life phenomenon well. Time series gene microarray data, which contains the temporal information, could help with the dynamic network reconstruction, as is indicated in the gene knock-out experiments by Geier et al [1]. In those experiments, the smaller the time interval is, the more accurate the result becomes. It is not desirable to make the interval too small, since the experiment data would be far more than enough when it comes to numerous gene observations
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