Visual Analysis of Regulatory Networks
نویسنده
چکیده
The amount of biological data obtained by new high-throughput technologies is growing exponentially, leading to the identification of huge regulatory networks. In this context, the analysis and interpretation of the relationships in these networks is becoming a major bottleneck in computational biology. Although some tools are already available to process and analyze biological networks, several difficulties arise when dealing with large regulatory networks involving thousands of protein interactions. One severe bottleneck is the visual analysis and the interpretation of these regulatory networks. In this context, the aim of this work was to develop a new visualization tool for biological networks which is able to represent and analyze large gene transcription networks, in a visual perspective. In this work, we used as a case study, the regulatory network on Saccharomyces cerevisiae, provided by YEASTRACT [1], which contains 6310 genes and more than 42000 interactions between them. Furthermore, we analyzed the same network in a graph mining point of view by studying its structure and by trying to find clusters of transcription factors, i.e. communities of genes that are strongly connected within each other, though sparsely connected among others. The search for communities within graphs representing regulatory networks, became a hot topic in molecular biology, based on the assumption that they may have the same functional roles within the cell. However, in our analysis, it was found that it is impossible to find clusters within the TF subnetwork, due to its complexity and structure. Even though, we observed that it was possible to filter sets of TFs that could provide better results when we try to address their cellular roles.
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تاریخ انتشار 2010