Computing trap space-based control strategies for Boolean networks using answer set programming

Abstract

Control of Boolean networks enables important medical and biological applications. At the core of many approaches is value percolation, by virtue of its simplicity and ease of implementation. Methods based uniquely on percolation can however miss many control strategies. We previously introduced a new method which, using the network's trap spaces, can uncover additional sets of interventions. In this work we present a highly efficient implementation of this methodology based on Answer Set Programming, allowing for simple and fast application to biological networks, as illustrated with some cases studies of cell reprogramming.