  The control of biological systems presents interesting applications such as
cell reprogramming or drug target identification. A common type of control
strategy consists in a set of interventions that, by fixing the values of some
variables, force the system to evolve to a desired state. This work presents a
new approach for finding control strategies in biological systems modeled by
Boolean networks. In this context, we explore the properties of trap spaces,
subspaces of the state space which the dynamics cannot leave. Trap spaces for
biological networks can often be efficiently computed, and provide useful
approximations of attraction basins. Our approach provides control strategies
for a target phenotype that are based on interventions that allow the control
to be eventually released. Moreover, our method can incorporate information
about the attractors to find new control strategies that would escape usual
percolation-based methods. We show the applicability of our approach to two
cell fate decision models.
