title: Construction and analysis of microRNA-transcription factor regulation network in Arabidopsis.
abstract: Transcription factors (TFs) and microRNAs (miRNAs) are two major types of regulators of gene expression, at transcriptional and post-transcriptional levels, respectively. By gathering their gene regulatory relationships, gene regulatory networks (GRNs) could be formed. A network motif is a type of connection pattern among a set of nodes which appears significantly more frequently than in random networks. Investigations of the network motifs often yield biological insights into the nature of the network. The previous study on miRNA-TF regulation networks concentrated on animals, and relied heavily on computational predictions. The authors collected data concerning miRNA regulation and transcriptional regulation relationships in Arabidopsis from publicly available databases, and further incorporated them with the protein-protein interaction data. All the data in the author's collection are supported by experiments. They screened the network motifs, whose size ranges between 1 and 4. The biological implications of the motifs were further analysed, and a flower development related network was constructed as an example. In this example, they illustrated the relevance of the network with the given process, and proposed the association of several genes with flowers by a network cluster identification. In this study, they analysed the properties of the GRN in Arabidopsis, and discussed their biological implications, as well as their potential applications.
authors.affiliation: Department of Applied Bioscience, College of Agronomy and Biotechnology, Hangzhou 310058, People's Republic of China. mchen@zju.edu.cn.
authors.forename: Lie
authors.initials: L
authors.lastname: Tang
authors.affiliation: Department of Bioinformatics, College of Life Sciences, Hangzhou, Zhejiang, People's Republic China.
authors.forename: Zhao
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Control Science and Engineering, Zhejiang University, Hangzhou 310058, People's Republic of China.
authors.forename: Peizhen
authors.initials: P
authors.lastname: Gu
authors.affiliation: Department of Bioinformatics, College of Life Sciences, Hangzhou, Zhejiang, People's Republic China.
authors.forename: Ming
authors.initials: M
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D016208:Databases, Factual; D030541:Databases, Genetic; D035264:Flowers; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks; D035683:MicroRNAs / Q000378:metabolism*; D025941:Protein Interaction Mapping; D012984:Software; D013045:Species Specificity; D014157:Transcription Factors / Q000378:metabolism*; D014158:Transcription, Genetic
chemical_list: D035683:MicroRNAs; D014157:Transcription Factors