HRGRN: Model and Decipher Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks using Graph Theory

The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only tens of thousands of genes, compounds, proteins, and RNAs but also the complicated interactions and coordination among them. These networks play critical roles in many fundamental functions, such as plant growth, development and environmental responses. Although much is known about these complex interactions, the knowledge and data are currently scattered throughout published literatures, third-party databases and publicly available high-throughput datasets. Many "unknown" yet important interactions among genes need to be mined and established through extensive computational analysis. However, exploring these complex biological interactions at genome-scale network level from existing heterogeneous resources remains challenging and time-consuming for biologists.

We developed a graph-search empowered web platform named HRGRN to search known and, more importantly, discover the new relationships among genes, proteins, compounds, and small RNAs in the signal transduction, metabolism, and gene regulatory networks in the model plant Arabidopsis thaliana. The HRGRN includes over 51,000 “nodes” that represent very large sets of genes, proteins, small RNAs, and compounds and approximately 150,000 “edges” that were defined/classified into nine types to comprehensively model the complex interactions between nodes. These include the interactions between proteins, compounds and proteins, transcription factors (TFs) and their downstream target genes, small RNAs and their target genes, kinases and downstream target genes, transporters and substrates, as well as substrate/product compounds and enzymes. The gene pairs with similar expression patterns were also defined as connected nodes, which will provide deep insight into gene-gene relationships. All the edges were extracted or predicted from various data sources, such as protein-protein interactions from thirty party databases based on two-hybrid experiments and homolog analysis, co-expressed gene pairs from computational analysis of transcriptome data, and sRNA-target gene pairs from experimental and computational analysis.

By applying various graph traversal algorithms, the HRGRN provides users a series of integrative analysis tools to discover novel interactions between genes and/or pathways, and build sub-networks from user-specified seed nodes based on the extensive collections of interactions stored in its back-end graph databases. HRGRN offers rich graphics and user-friendly web interfaces that allow users to search, analyze, and visualize their query results. The HRGRN database is freely available at http://plantgrn.noble.org/hrgrn/.