Biological organisms exist within environments in which complex, non-linear dynamics are ubiquitous. They are coupled to these environments via their own complex, dynamical networks of enzyme-mediated reactions, known as biochemical networks. These networks, in turn, control the growth and behaviour of an organism within its environment. In this paper, we consider computational models whose structure and function are motivated by the organisation of biochemical networks. We refer to these as artificial biochemical networks, and show how they can be evolved to control trajectories within three behaviourally diverse complex dynamical systems: the Lorenz system, Chirikovs standard map, and legged robot locomotion. More generally, we consider the notion of evolving dynamical systems to control dynamical systems, and discuss the advantages and disadvantages of using higher order coupling and configurable dynamical modules (in the form of discrete maps) within artificial biochemical networks. We find both approaches to be advantageous in certain situations, though note that the relative trade-offs between different models of artificial biochemical network strongly depend on the type of dynamical systems being controlled.
@article(SS-TEVC-13, author = "Michael A. Lones and Luis A. Fuente and Alexander P. Turner and Leo S. D. Caves and Susan Stepney and Stephen L. Smith and Andy M. Tyrrell", title = "Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems", journal = "IEEE Transactions on Evolutionary Computation", volume = 18, number = 2, pages = "145-166", year = 2014 )