Discovery of Gene Regulatory Networks in Aspergillus fumigatus
Aspergillus fumigatus is the most important airborne fungal pathogen causing life-threatening infections in immunosuppressed patients. During the infection process, A. fumigatus has to cope with a dramatic change of environmental conditions, such as temperature shifts. Recently, gene expression data monitoring the stress response to a temperature shift from 30 °C to 48 °C was published. In the present work, these data were analyzed by reverse engineering to discover gene regulatory mechanisms of temperature resistance of A. fumigatus. Time series data, i.e. expression profiles of 1926 differentially expressed genes, were clustered by fuzzy c-means. The number of clusters was optimized using a set of optimization criteria. From each cluster a representative gene was selected by text mining in the gene descriptions and evaluating gene ontology terms. The expression profiles of these genes were simulated by a differential equation system, whose structure and parameters were optimized minimizing both the number of non-vanishing parameters and the mean square error of model fit to the microarray data.
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