GPU-accelerated steady-state analysis of probabilistic Boolean networks

Using PBNs for the modelling and analysis of biological systems can lead to a deeper understanding of the dynam- ics and behaviour of these systems (see Section ‘Dynamics of

In fact, approximations with Markov Chain Monte Carlo (MCMC) techniques remain the only feasible method to solve the problem. In [ 6 ], we have considered the two- state Markov

6 In our experiments, the maximum number of parent nodes in one group is set to 18. Similar to the value of k in Step 2, the number can be larger as long as the memory can

We note that for certain BNs, the minimal global control for moving to a target attractor A t computed by combining the minimal local control for the blocks might result in a

Based on the analysis we also showed that for a large class of models the annealed approximation does provide exact results for both magnetization and overlap order parameters;

We compute the seven different steady-state probabilities using three different methods: the sequential two-state Markov chain approach, the GPU-accelerated parallelisation

In the previous sections we saw that both notions of local monotonicity and of permutable lattice derivatives lead to two hierarchies of pseudo-Boolean functions which are related

- Kauffman’s model is a Boolean network having k quenched random connections per node, and evolving deterministically in time according to a quenched random rule.. They