The aim of this project is to understand how biological organisms process signals and how such an understanding might impinge on the future development of man-made devices. We normally associate computation with man-made devices, particularly the ubiquitous digital computer or the lesser-known analog computer. However there are computational devices much closer to home. All biological organisms are exposed almost continuously to a huge variety of environmental changes and shocks. In order to survive such changes, living organisms have evolved sensor proteins located on the outer surface of the organism which can detect all manner of environmental changes. These sensor proteins are in turn connected to so-called signaling networks composed of interacting proteins inside the cell. These signaling networks are responsible to making an appropriate decision based on all the sensory inputs. What is not well understood at this stage is the type of decision processing that is carried out by these networks. In man made devices we employ a variety of techniques from digital computers to analog devices to control our machines. Over the last fifty years or so, the design of sophisticated man-made control devices has matured to the extent that almost all devices now have some kind of control systems built into them. The reason why we are so good at designing artificial control systems is that we have a thorough grasp of the underlying theoretical principles of control. The primary technology that we used to build control devices is based on electronics. Walk into any book store and one will find books on electronic design. In relation to biological control systems we do not have the equivalent of an electronics design handbook. As a result we understand very little about how biological control systems work, how they carry out decisions and what the underlying principles of biological control are. Our approach is to evolve on digital computers, artificial biological signaling networks. Depending on what task the network is designed to perform, we evolve networks which will come closest to achieving this objective. Examples include evolving a network which can be robust to sudden changes in the environment, or conversely evolving networks which can quickly respond to environmental changes. In addition other objectives will include common signal processing techniques employed in electronics, for example we might evolve a network that can oscillate or a network that can carry out some arithmetic. By these means we will generate biological like networks which will have the capacity to carry out all the common electronic signal processing tasks. The end results will be a large library of networks. From this library we will then reverse engineer the networks to understand how they accomplish their evolved tasks. Finally we will compare these networks to real biological networks to see if we can find equivalent 'designs'. The ultimate goal is to write the 'electronics' design manual of biological signaling control networks. The work we propose in this application impinges on many areas of science. It combines work from molecular biology, computer science, control theory, evolutionary algorithms, signal processing and electrical circuit theory. The engineering sciences will benefit from this work by being able to examine examples of signal processing carried out at the molecular level and the biological sciences will benefit by an understanding of the underlying control principles of real biological networks. In addition, molecular based circuitry has to deal with noise (which is dealt with extensively in the engineering sciences), this work may have an important bearing on the implementation of nanotechnology based control systems.
