  Genetic regulatory networks are defined by their topology and by a multitude
of continuously adjustable parameters. Here we present a class of simple models
within which the relative importance of topology vs. interaction strengths
becomes a well-posed problem. We find that complexity - the ability of the
network to adopt multiple stable states - is dominated by the adjustable
parameters. We comment on the implications for real networks and their
evolution.
