NSF Award
0432190
The aim of this project is to understand how biological organisms process signals and how such<br/>an understanding might impinge on the future development of man-made devices.<br/>We normally associate computation with man-made devices, particularly the ubiquitous digital<br/>computer or the lesser-known analog computer. However there are computational devices much<br/>closer to home. All biological organisms are exposed almost continuously to a huge variety of<br/>environmental changes and shocks. In order to survive such changes, living organisms have<br/>evolved sensor proteins located on the outer surface of the organism which can detect all manner<br/>of environmental changes. These sensor proteins are in turn connected to so-called signaling<br/>networks composed of interacting proteins inside the cell. These signaling networks are<br/>responsible to making an appropriate decision based on all the sensory inputs. What is not well<br/>understood at this stage is the type of decision processing that is carried out by these networks. In<br/>man made devices we employ a variety of techniques from digital computers to analog devices<br/>to control our machines. Over the last fifty years or so, the design of sophisticated man-made<br/>control devices has matured to the extent that almost all devices now have some kind of control<br/>systems built into them. The reason why we are so good at designing artificial control systems is<br/>that we have a thorough grasp of the underlying theoretical principles of control. The primary<br/>technology that we used to build control devices is based on electronics. Walk into any book<br/>store and one will find books on electronic design.<br/>In relation to biological control systems we do not have the equivalent of an electronics design<br/>handbook. As a result we understand very little about how biological control systems work, how<br/>they carry out decisions and what the underlying principles of biological control are. Our<br/>approach is to evolve on digital computers, artificial biological signaling networks. Depending<br/>on what task the network is designed to perform, we evolve networks which will come closest to<br/>achieving this objective. Examples include evolving a network which can be robust to sudden<br/>changes in the environment, or conversely evolving networks which can quickly respond to<br/>environmental changes. In addition other objectives will include common signal processing<br/>techniques employed in electronics, for example we might evolve a network that can oscillate or<br/>a network that can carry out some arithmetic. By these means we will generate biological like<br/>networks which will have the capacity to carry out all the common electronic signal processing<br/>tasks. The end results will be a large library of networks. From this library we will then reverse<br/>engineer the networks to understand how they accomplish their evolved tasks. Finally we will<br/>compare these networks to real biological networks to see if we can find equivalent 'designs'.<br/>The ultimate goal is to write the 'electronics' design manual of biological signaling control<br/>networks.<br/>The work we propose in this application impinges on many areas of science. It combines work<br/>from molecular biology, computer science, control theory, evolutionary algorithms, signal<br/>processing and electrical circuit theory.<br/>The engineering sciences will benefit from this work by being able to examine examples of<br/>signal processing carried out at the molecular level and the biological sciences will benefit by an<br/>understanding of the underlying control principles of real biological networks. In addition,<br/>molecular based circuitry has to deal with noise (which is dealt with extensively in the<br/>engineering sciences), this work may have an important bearing on the implementation of<br/>nanotechnology based control systems.
