Evolving Boolean networks for biological control: State space targeting in scale free Boolean networks

Abstract

Gene regulatory networks are the complex dynamical structures that orchestrate the activities of biological cells. Inappropriate dynamical behaviours, caused by mutations or environmental perturbations, can lead to disease. Control interventions, for example in the form of therapeutic drugs, can lead to recovery from disease. In this paper, we consider how Boolean networks can be used to control a computational model of gene regulatory networks, focusing on the problem of state space targeting in scale-free Boolean networks, an abstract yet realistic model of biological gene regulatory networks. Our results suggest that Boolean networks can be optimised to carry out useful control, and that the approach is relatively scalable. We also take an initial look at the trade-off between the efficacy and efficiency of control, showing that many target networks can be controlled via a relatively small degree of coupling, giving hope that Boolean network controllers could one day be implemented in vivo.