Statistical Properties of Randomly Constructed Boolean Networks that Resemble the Transcription Factor Network of Escherichia coli

Abstract

The complete genome sequence has been determined for many microbial species, including Escherichia coli, the best-studied of model organisms. This information, together with the global patterns of gene expression revealed by microarray technology, permits a “top-down” approach to understanding the integrated genetic network of an organisms. This global view is in contrast to the “bottom-up” approach of traditional molecular biology, which builds upon the detailed characterization of the individual component parts of the organisms. A third approach to understanding genetic networks, first introduced by Stuart Kauffman [2], is to construct networks at random and ask what behaviors are expected to arise with high probability, independent of selection.