Information content based model for the topological properties of the gene regulatory network of Escherichia coli

TL;DR

This paper introduces a content-based null model for the E. coli gene regulatory network, successfully replicating key topological features and providing insights into its evolutionary processes.

Contribution

It applies a novel content-based modeling approach to E. coli's GRN, achieving realistic replication of its topological properties and offering a new perspective on network evolution.

Findings

The model reproduces the exponential in-degree distribution.

It suggests a power-law out-degree distribution with a flat tail.

Qualitative agreement with empirical network properties.

Abstract

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.