A new sensitivity measure for probabilistic Boolean networks based on steady-state distributions

Probabilistic Boolean networks model biological processes with the network dynamics. This paper studies the network sensitivity with respect to perturbations to networks, including regulatory rules and the involved parameters, in the long run. We define the network sensitivity based on the steady-state distributions of probabilistic Boolean networks as their underlying model is a finite Markov chain. The steady-state distribution reflects the long-run behavior of the network and the change of steady-state distribution caused by possible perturbations is the key measure for intervention.