Dynamical Criticality in Growing Networks

The principle of dynamical criticality is a very important hypotheses in biology, and it therefore deserves a thorough investigation. Testing the principle in real biological cases can be far from trivial: therefore, in this work we make use of the Random Boolean Network framework, which has been extensively used to model genetic regulatory networks, and which has since become one of the most used models in the field of complex systems. We subject several RBN ensembles to evolutionary changes: the key research questions are whether initially critical networks will grow faster than ordered or chaotic ones, and whether evolution can influence the dynamic regime, and in which direction. The results obtained so far indicate that critical systems perform well in the analyzed tasks. In the case of two connections per node, the best performances are those of critical systems, while increasing the value of the connectivity there seems to be a slight shift towards more disordered regimes (albeit still close to the critical one).