  Mathematical modeling of biological networks can help to integrate a large
body of information into a consistent framework, which can then be used to
arrive at novel mechanistic insight and predictions. We have previously
developed a detailed, mechanistic model for the regulation of {\sigma}F during
sporulation in Bacillus subtilis. The model was based on a wide range of
quantitative data, and once fitted to the data, the model made predictions that
could be confirmed in experiments. However, the analysis was based on a single
optimal parameter set. We wondered whether the predictions of the model would
be stable for all optimal parameter sets. To that end we conducted a global
parameter screen within the physiological parameter ranges. The screening
approach allowed us to identify sensitive and sloppy parameters, and
highlighted further required datasets during the optimization. Eventually, all
parameter sets that reproduced all available data predicted the physiological
situation correctly.
