Learning Gene Regulatory Networks with Predefined Attractors for Sequential Updating Schemes Using Simulated Annealing

A simulated annealing framework is presented for learning gene regulatory networks with predefined attractors, under the threshold Boolean network model updated sequentially. The proposed method is used to study the robustness of the networks, defined as the number of different updating sequences they can have without loosing the attractor. The results suggests a power law between the frequency of the networks and the number of the updating sequences, also, a decrease of the networks´ robustness as the cycle length grows. In general, the proposed simulated annealing framework is effective for reverse engineering problems.