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.