  Gene regulatory networks (GRN) are being studied with increasingly precise
quantitative tools and can provide a testing ground for ideas regarding the
emergence and evolution of complex biological networks. We analyze the global
statistical properties of the transcriptional regulatory network of the
prokaryote Escherichia coli, identifying each operon with a node of the
network. We propose a null model for this network using the content-based
approach applied earlier to the eukaryote Saccharomyces cerevisiae. (Balcan et
al., 2007) Random sequences that represent promoter regions and binding
sequences are associated with the nodes. The length distributions of these
sequences are extracted from the relevant databases. The network is constructed
by testing for the occurrence of binding sequences within the promoter regions.
The ensemble of emergent networks yields an exponentially decaying in-degree
distribution and a putative power law dependence for the out-degree
distribution with a flat tail, in agreement with the data. The clustering
coefficient, degree-degree correlation, rich club coefficient and k-core
visualization all agree qualitatively with the empirical network to an extent
not yet achieved by any other computational model, to our knowledge. The
significant statistical differences can point the way to further research into
non-adaptive and adaptive processes in the evolution of the E. coli GRN.
