Markov chain of the transition probabilities between the steady state and the state cycle in our simple regulatory network. Let k be the number of states within an attractor (k can be 1 in the case of a point attractor) and N be the number of species in the model. For each attractor of this finite time-homogeneous Markov chain, we perturb each species of each state by triggering its own Boolean function asynchronously. Thus there are kN perturbations per attractor. In our simple regulatory network the cyclic attractor has 3 states. The number of species in the model is 4. The weight of the edge from an attractor X to an attractor Y is the ratio between the number of perturbations of X which lead to Y over the total number of perturbations of X.