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

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.

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

“…Apart from identifying core regulatory molecules it is also important to organize a GRN in a structure that helps in understanding the flow of regulatory information. Following previous studies on yeast and bacterial GRNs [ 42 , 43 ], we used the K-core algorithm to organize the regulatory molecules in MCF-7 estrogen response GRN in a layered hierarchy. The K-core algorithm is generally used for identifying a set of central or K-core nodes in a network all of which have a degree of at least K. It works by iteratively removing leaf nodes which have degree less than K (all nodes with degree one are removed in the first iteration, nodes with degree two are removed in the second iteration, and so forth) so that in the final or K’th iteration only the K-core set of nodes remains.…”

“…We identified the hierarchy of genes based not only on their immediate regulatory interactions but also on their overall importance within the network. We used K-shell (also known as K-core) decomposition, a classical method in graph theory [ 69 ] which has been used to obtain a hierarchy of nodes based on their degree characteristics in bacteria and yeast GRNs [ 42 , 43 ]. Each K-shell was obtained by successively removing nodes of degree K beginning with degree 1.…”

Characterization of regulatory features of housekeeping and tissue-specific regulators within tissue regulatory networks

“…For example, Han et al [12] and Taylor et al [13] found that removal of two classes of hubs could strongly affect the organization of protein interaction networks, while analysis of network dynamics for multiple processes by Luscombe et al [14] revealed large topological changes in regulatory networks. Gerstein et al [4] found that factors at different in-degree or out-degree levels in a regulatory network have different biological functions and Bhardwaj et al [15-17] showed that factors with different hierarchies in model organism regulatory networks have different properties. Specifically, Lin et al [18] revealed that housekeeping (HK) and tissue-specific (TS) proteins have their own structural organization in human protein interaction networks.…”