Node and edge control strategy identification via trap spaces in Boolean networks

The study of control mechanisms of biological systems allows for interesting applications in bioengineering and medicine, for instance in cell reprogramming or drug target identification. A control strategy often consists of a set of interventions that, by fixing the values of some components, ensure that the long term dynamics of the controlled system is in a desired state. A common approach to control in the Boolean framework consists in checking how the fixed values propagate through the network, to establish whether the effect of percolating the interventions is sufficient to induce the target state. Although methods based uniquely on value percolation allow for efficient computation, they can miss many control strategies. Exhaustive methods for control strategy identification, on the other hand, often entail high computational costs. In order to increase the number of control strategies identified while still benefiting from an efficient implementation, we introduce the use of trap spaces, subspaces of the state space that are closed with respect to the dynamics, and that can usually be easily computed in biological networks.

This work presents a method based on value percolation that uses trap spaces to uncover new control strategies. It allows for node interventions, which fix the value of certain components, and edge interventions, which fix the effect that one component has on another. The method is implemented using Answer Set Programming, extending an existing efficient implementation of value percolation to allow for the use of trap spaces and edge control. The applicability of the approach is studied for different control targets in a biological case study, identifying in all cases new control strategies.

The method presented here provides a new tool for control strategy identification in Boolean networks that allows for more diversity of interventions and for the possibility of efficiently finding new control strategies that would escape usual percolation-based methods, widening the possibility for potential applications.