Artificial Biochemical Networks:

Previous work by ourselves and others has shown how the structure and organisation of biological organisms can
motivate the design of computer hardware and software, with the aim of capturing useful properties such as complex
information processing and resistance to environmental perturbation. This proposal focuses upon one of the most
complex sets of structures found in biological systems: biochemical networks. These structures are fundamental to the
development, function and evolution of biological organisms, and are the main factor underlying the complexity seen
within higher organisms. Previous attempts to build hardware and software systems motivated by these structures has led
to a group of computer architectures which we collectively refer to as artificial biochemical network models. The best
known of these is the artificial genetic network, which has shown itself to be an effective means of expressing complex
computational behaviours, particularly within robotic control. Nevertheless, this field of research has received relatively
little attention, and little is known about the computational properties of these architectures. The aim of the proposed work
is to develop better artificial biochemical network models, which we will do by both bringing together existing work and
introducing new understanding from the biological sciences. We will also develop a theoretical framework to better
understand what these computational architectures are capable of, and show how how these models can be applied to
the difficult problem of controlling a robot in real world environments. It is expected that this work will also produce
insights into the function and evolution of the biological systems on which the architectures are modelled.