Genomic analysis of the hierarchical structure of regulatory networks

Abstract

A fundamental question in biology is how the cell uses transcription factors (TFs) to coordinate the expression of thousands of genes in response to various stimuli. The relationships between TFs and their target genes can be modeled in terms of directed regulatory networks. These relationships, in turn, can be readily compared with commonplace ''chain-of-command'' structures in social networks, which have characteristic hierarchical layouts. Here, we develop algorithms for identifying generalized hierarchies (allowing for various loop structures) and use these approaches to illuminate extensive pyramid-shaped hierarchical structures existing in the regulatory networks of representative prokaryotes (Escherichia coli) and eukaryotes (Saccharomyces cerevisiae), with most TFs at the bottom levels and only a few master TFs on top. These masters are situated near the center of the protein-protein interaction network, a different type of network from the regulatory one, and they receive most of the input for the whole regulatory hierarchy through protein interactions. Moreover, they have maximal influence over other genes, in terms of affecting expression-level changes. Surprisingly, however, TFs at the bottom of the regulatory hierarchy are more essential to the viability of the cell. Finally, one might think master TFs achieve their wide influence through directly regulating many targets, but TFs with most direct targets are in the middle of the hierarchy. We find, in fact, that these midlevel TFs are "control bottlenecks" in the hierarchy, and this great degree of control for "middle managers" has parallels in efficient social structures in various corporate and governmental settings.organization ͉ topology ͉ transcriptional regulation ͉ yeast

SND1 Transcription Factor-Directed Quantitative Functional Hierarchical Genetic Regulatory Network in Wood Formation in Populus trichocarpa

“…A combination of ChIP sequencing or ChIP-PCR and the TF-induced differential gene expression is needed to reveal the functional network and its regulatory effects. This combination has been used to discover specific functional regulatory networks in animal development, using cell cultures representing different developmental stages where specific sets of TFs are induced (Yu and Gerstein, 2006;Farnham, 2009;Bhardwaj et al, 2010;Gerstein et al, 2010;Roy et al, 2010;Cheng et al, 2011). These TF regulatory networks are arranged in well-organized hierarchies.…”

“…For tree species that are amenable to genetic transformation, methods are technically demanding and slow, requiring 12 to 18 months of tissue culture (Merkle and Dean, 2000). To reveal a functional hGRN for wood formation, an efficient transgenic system, such as those developed for the cell cultures of yeast (Saccharomyces cerevisiae) and animals (Horak et al, 2002;Yu and Gerstein, 2006;Gerstein et al, 2010;Cheng et al, 2011;Niu et al, 2011), is needed where immediate transcriptome responses to TF perturbation can be induced, characterized, and quantified.…”

A Least Squares Fitting-Based Modeling of Gene Regulatory Sub-networks

“…We also verified their summary from the Saccharomyces Genome Database (SGD) 3 . While identifying the hierarchical structure of regulatory networks [8] it was reported that Y HR084W -Y NL192W forms a TF-T gene pair. One can also arrive at similar established conclusions for the other TF-T pairs (obtained in different biclusters).…”

Improved Bayesian Network inference using relaxed gene ordering

“…The backbone, i.e., network layer information, can be used as a biological constraint to facilitate a more efficient Bayesian structural learning as presented below. Yu and Gerstain (2006) manually assembled a Saccharomyces cerevisiae gene regulatory network by combining the interaction data from various genetic, biochemical and ChIP-chip experiments. Applying Breadth-First Search (BFS) on the network, they discovered that the network is organised into four hierarchical layers.…”

“…The algorithm exploits a state-of-art biological constraint, namely, signal is transmitted sequentially and hierarchically from the master regulators on the 'top layer' to the regulators in the subsequent layers (Yu and Gerstain, 2006;Cosentino et al, 2007). Our approach selects subset of context-dependent genes, and infers optimal joint regulatory relationships among genes using a microarray data set describing a specific biological process.…”