  Understanding control mechanisms in biological systems plays a crucial role
in important applications, for instance in cell reprogramming. Boolean modeling
allows the identification of possible efficient strategies, helping to reduce
the usually high and time-consuming experimental efforts. Available approaches
to control strategy identification usually focus either on attractor or
phenotype control, and are unable to deal with more complex control problems,
for instance phenotype avoidance. They also fail to capture, in many
situations, all possible minimal strategies, finding instead only sub-optimal
solutions. In order to fill these gaps, we present a novel approach to control
strategy identification in Boolean networks based on model checking. The method
is guaranteed to identify all minimal control strategies, and provides maximal
flexibility in the definition of the control target. We investigate the
applicability of the approach by considering a range of control problems for
different biological systems, comparing the results, where possible, to those
obtained by alternative control methods.
