A Decomposition-based Approach towards the Control of Boolean Networks (Technical Report)

TL;DR

This paper presents a decomposition-based method for controlling large asynchronous Boolean networks by efficiently identifying minimal control node sets, outperforming existing methods in scalability and speed.

Contribution

The paper introduces a novel decomposition approach that leverages network structure and dynamics to efficiently solve the minimal control problem in large Boolean networks.

Findings

The method significantly reduces computation time on large networks.

It successfully controls real-life biological networks.

The approach outperforms existing global methods in scalability.

Abstract

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be controlled to drive its dynamics from an initial steady state (or attractor) to a target steady state. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking such an approach, we derive a decomposition-based solution to the minimal control problem which can be significantly faster than the existing approaches on large networks. We apply our solution to both real-life biological networks and randomly generated networks, demonstrating promising results.