  Advances in imaging technology now provide us with detailed 3D data on gene
expression patterns in developing embryos. This information can be used to
build predictive mathematical models of embryogenesis. Current modelling
approaches are, however, limited by lack of methods to automatically infer the
regulatory networks and the parameter values from the image-based information.
Here we make a first step to the development of such methods. We use limb bud
development as a model system. For a given regulatory network we developed a
decision tree based algorithm to automatically determine parameter values for
which the model reproduces the expression patterns. Starting from this
parameter set, local optimization was performed to further reduce the chosen
goodness-of-fit measure. This approach allowed us to recover the target
expression patterns, as judged by eye, and thus provides a first step towards
the automated inference of parameter values for a given regulatory network.
