Discovery of gene regulatory networks in aspergillus fumigatus

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