Inference of gene regulatory subnetworks from time course gene expression data.
BACKGROUND
Identifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN reconstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to others in the sub-networks.


RESULTS
We propose the network and community identification (NCI) method for identifying multiple subnetworks from gene expression data by incorporating community structure information into GRN inference. The proposed algorithm iteratively solves two optimization problems, and can promisingly be applied to large-scale GRNs. Furthermore, we present the efficient Block PCA method for searching communities in GRNs.


CONCLUSIONS
The NCI method is effective in identifying multiple subnetworks in a large-scale GRN. With the splitting algorithm, the Block PCA method shows a promosing attempt for exploring communities in a large-scale GRN.
School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.
Xi-Jun
XJ
Liang
Zhonghang
Z
Xia
Li-Wei
LW
Zhang
Fang-Xiang
FX
Wu
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D053263:Gene Regulatory Networks