  The process of stochastic Turing instability on a network is discussed for a
specific case study, the stochastic Brusselator model. The system is shown to
spontaneously differentiate into activator-rich and activator-poor nodes,
outside the region of parameters classically deputed to the deterministic
Turing instability. This phenomenon, as revealed by direct stochastic
simulations, is explained analytically, and eventually traced back to the
finite size corrections stemming from the inherent graininess of the
scrutinized medium.
