Contact:          Habtom Ressom, Stellos Tavantzis

Project Summary

Functional genomics is a rapidly emerging discipline involving large-scale exploration of gene function with new high-throughput technologies yielding massive amounts of gene expression data. With this comes the need for more sophisticated ways of sorting the data for further analysis and study. This project will bring together scientists from molecular biology and computer engineering to generate and analyze gene expression data from an important plant pathogenic fungus, Rhizoctonia solani . A novel clustering algorithm that can accomplish data partitioning and automatic detection of the best number of clusters simultaneously will be developed using advanced computational methods. The algorithm will enable us to effectively organize large-scale gene expression data generated from studying the phenomenon of hypovirulence (less-than-normal disease-producing capacity) in R. solani . The goal is to identify genes that are active in R. solani under different conditions that up- or down-regulate virulence (disease-producing capacity). This will be achieved using macroarrays, arrays of genes representing the entire genetic makeup of R. solani by spotting clones of these genes onto nylon membranes, and complex probes comprised of radiolabeled copies of genes activated differentially under these conditions. The genes that are co-regulated under varying degrees of virulence will be determined by clustering the macroarray data using the proposed novel clustering method. Understanding the genetic regulation of virulence in R. solani will allow us to manage this pathogen using genetic means as compared to the currently used chemical pollutants.

Return to Project Index