  Boolean network models of strongly connected modules are capable of capturing
the high regulatory complexity of many biological gene regulatory circuits. We
study numerically the previously introduced basin entropy, a parameter for the
dynamical uncertainty or information storage capacity of a network as well as
the average transient time in random relevant components as a function of their
connectivity. We also demonstrate that basin entropy can be estimated from
time-series data and is therefore also applicable to non-deterministic networks
models.
