  Random boolean networks are a model of genetic regulatory networks that has
proven able to describe experimental data in biology. They not only reproduce
important phenomena in cell dynamics, but they are also extremely interesting
from a theoretical viewpoint, since it is possible to tune their asymptotic
behaviour from order to disorder. The usual approach characterizes network
families as a whole, either by means of static or dynamic measures. We show
here that a more detailed study, based on the properties of system's
attractors, can provide information that makes it possible to predict with
higher precision important properties, such as system's response to gene
knock-out. A new set of principled measures is introduced, that explains some
puzzling behaviours of these networks. These results are not limited to random
Boolean network models, but they are general and hold for any discrete model
exhibiting similar dynamical characteristics.
