  We study the problem of computing a minimal subset of nodes of a given
asynchronous Boolean network that need to be controlled to drive its dynamics
from an initial steady state (or attractor) to a target steady state. Due to
the phenomenon of state-space explosion, a simple global approach that performs
computations on the entire network, may not scale well for large networks. We
believe that efficient algorithms for such networks must exploit the structure
of the networks together with their dynamics. Taking such an approach, we
derive a decomposition-based solution to the minimal control problem which can
be significantly faster than the existing approaches on large networks. We
apply our solution to both real-life biological networks and randomly generated
networks, demonstrating promising results.
