Boolean networks (BNs) are discrete dynamical systems with applications to
the modeling of cellular behaviors. In this paper, we demonstrate how the
software BoNesis can be employed to exhaustively identify combinations of
perturbations which enforce properties on their fixed points and attractors. We
consider marker properties, which specify that some components are fixed to a
specific value. We study 4 variants of the marker reprogramming problem: the
reprogramming of fixed points, of minimal trap spaces, and of fixed points and
minimal trap spaces reachable from a given initial configuration with the most
permissive update mode. The perturbations consist of fixing a set of components
to a fixed value. They can destroy and create new attractors. In each case, we
give an upper bound on their theoretical computational complexity, and give an
implementation of the resolution using the BoNesis Python framework. Finally,
we lift the reprogramming problems to ensembles of BNs, as supported by
BoNesis, bringing insight on possible and universal reprogramming strategies.
This paper can be executed and modified interactively.Comment: Notebook available at
https://nbviewer.org/github/bnediction/reprogramming-with-bonesis/blob/release/paper.ipyn