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.