  In the scope of discrete finite-state models of interacting components, we
present a novel algorithm for identifying sets of local states of components
whose activity is necessary for the reachability of a given local state. If all
the local states from such a set are disabled in the model, the concerned
reachability is impossible. Those sets are referred to as cut sets and are
computed from a particular abstract causality structure, so-called Graph of
Local Causality, inspired from previous work and generalised here to finite
automata networks. The extracted sets of local states form an
under-approximation of the complete minimal cut sets of the dynamics: there may
exist smaller or additional cut sets for the given reachability. Applied to
qualitative models of biological systems, such cut sets provide potential
therapeutic targets that are proven to prevent molecules of interest to become
active, up to the correctness of the model. Our new method makes tractable the
formal analysis of very large scale networks, as illustrated by the computation
of cut sets within a Boolean model of biological pathways interactions
gathering more than 9000 components.

Systems and Control