Optimising Boolean Synthetic Regulatory Networks to Control Cell States

Controlling the dynamics of gene regulatory networks is a challenging problem. In recent years, a number of control methods have been proposed, but most of these approaches do not address the problem of how they could be implemented in practice. In this paper, we consider the idea of using a synthetic regulatory network as a closed-loop controller that can control and respond to the dynamics of a cell's native regulatory network in situ. We explore this idea using a computational model in which both native and synthetic regulatory networks are represented by Boolean networks. We then use an evolutionary algorithm to optimise both the structure and parameters of the synthetic Boolean network. To test this approach, we look at whether controllers can be optimised to target specific steady states in five different Boolean regulatory circuit models. Our results show that in most cases the controllers are able to drive the dynamics of the target system to a specified steady state, often using few interventions, and further experiments using random Boolean networks show that the approach scales well to larger controlled networks.