  Regulatory networks (RNs) are a well-accepted modelling formalism in
computational systems biology. The control of RNs is currently receiving a lot
of attention because it provides a computational basis for cell reprogramming
-- an attractive technology developed in regenerative medicine. By solving the
control problem, we learn which parts of a biological system should be
perturbed to stabilise the system in the desired phenotype.
  We allow the specification of the Boolean model representing a given RN to be
incomplete. To that end, we utilise the formalism of partially specified
Boolean networks which covers every possible behaviour of unspecified parts of
the system. Such an approach causes a significant state explosion. This problem
is addressed by using symbolic methods to represent both the unspecified model
parts and all possible perturbations of the system.
  Additionally, to make the control design efficient and practically
applicable, the optimal control should be minimal in terms of size. Moreover,
in a partially specified model, a control may achieve the desired stabilisation
only for a subset of the possible fully specified model instantiations. To
address these aspects, we utilise several quantitative measures. Apart from the
size of perturbation, we also examine its robustness -- a portion of
instantiations for which the control is applicable.
  We show that proposed symbolic methods solving the control problem for
partially specified BNs are efficient and scale well. We also evaluate the
robustness metrics in cases of all three studied control types. The robustness
metric tells us how big a proportion of fully defined systems the given
perturbation works. Our experiments support the hypothesis that one-step
perturbations may be less robust than temporary or permanent perturbations.
  This is a full version of a paper that is submitted to a journal.
