Towards intelligent biological control: Controlling boolean networks with boolean networks

Abstract

Gene regulatory networks (GRNs) are the complex dynamical systems that orchestrate the activities of biological cells. In order to design effective therapeutic interventions for diseases such as cancer, there is a need to control GRNs in more sophisticated ways. Computational control methods offer the potential for discovering such interventions, but the difficulty of the control problem means that current methods can only be applied to GRNs that are either very small or that are topologically restricted. In this paper, we consider an alternative approach that uses evolutionary algorithms to design GRNs that can control other GRNs. This is motivated by previous work showing that computational models of GRNs can express complex control behaviours in a relatively compact fashion. As a first step towards this goal, we consider abstract Boolean network models of GRNs, demonstrating that Boolean networks can be evolved to control trajectories within other Boolean networks. The Boolean approach also has the advantage of a relatively easy mapping to synthetic biology implementations, offering a potential path to in vivo realisation of evolved controllers.