Automatic Screening for Perturbations in Boolean Networks - Schwab, Julian D.; Kestler, Hans A. (Frontiers in Physiology, 2018)
A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behaviour of the modelled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behaviour - so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviours. Changing internal and external conditions can influence the long-term behaviour of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behaviour can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilising satisfiability (SAT) solvers for fast exhaustive attractor search.