Preliminary studies on the in silico evolution of biochemical networks.

Due to the variety and importance of roles performed by signalling networks, understanding their function and evolution is of great interest. Signalling networks allow organisms to process and react to changes in their internal and external environment. Current estimates suggest that two to three percent of all genomes code for proteins involved in signalling networks. The study of signalling networks is hindered by the complexities of the networks and difficulties in ascribing function to form. For example, a very complex dense network might comprise eighty or more densely connected proteins. In the majority of cases there is very little understanding of how these networks process signals. Unlike in electronics, where there is a broad practical and theoretical understanding of how to construct devices that can process almost any kind of signal, in biological signalling networks there is no equivalent theory. Part of the problem stems from the fact that in most cases it is unknown what particular signal processing circuits would look like in a biological form. This paper describes the evolutionary methods used to generate networks with particular signal- and computational-processing capabilities. The techniques involved are described, and the approach is illustrated by evolving computational circuits such as multiplication, radicals and logarithmic functions. The experiments also illustrate the evolution of modularity within biochemical reaction networks.