Topological and causal structure of the yeast transcriptional regulatory network

Abstract

Interpretation of high-throughput biological data requires a knowledge of the design principles underlying the networks that sustain cellular functions. Of particular importance is the genetic network, a set of genes that interact through directed transcriptional regulation. Genes that exert a regulatory role encode dedicated transcription factors (hereafter referred to as regulating proteins) that can bind to specific DNA control regions of regulated genes to activate or inhibit their transcription. Regulated genes may themselves act in a regulatory manner, in which case they participate in a causal pathway. Looping pathways form feedback circuits. Because a gene can have several connections, circuits and pathways may crosslink and thus represent connected components. We have created a graph of 909 genetically or biochemically established interactions among 491 yeast genes. The number of regulating proteins per regulated gene has a narrow distribution with an exponential decay. The number of regulated genes per regulating protein has a broader distribution with a decay resembling a power law. Assuming in computergenerated graphs that gene connections fulfill these distributions but are otherwise random, the local clustering of connections and the number of short feedback circuits are largely underestimated. This deviation from randomness probably reflects functional constraints that include biosynthetic cost, response delay and differentiative and homeostatic regulation.In integrating genome-wide data on transcript abundance 1 into a dynamic view of gene networks, recent studies have focused on abstracting the principles that underlie the architecture and causal interplay of these networks. At present, the yeast Saccharomyces cerevisiae is the most suitable eukaryotic organism for achieving this goal, as much information about its transcriptional regulations has been accumulated 2,3 . Of roughly 6,000 yeast genes, 124 have been shown through genetic and biochemical experiments to encode regulating proteins that can influence the expression of specific genes 2 . These data were obtained from a previous review 2 and were validated and updated, until July 2001, by manual inspection of the websites of MIPS, SwissProt, Yeast Protein Database, S. cerevisiae Promoter Database and the Saccharomyces Genome Database (see Web Note A online). The elements of the general transcription initiation machinery were excluded from this study, although some have differential roles in transcription of large subsets of genes 3 . Some of the 124 regulatory genes transcriptionally control a set of 367 non-regulatory genes ( Fig. 1) through 837 connections (see Web Table A online). Of the 124 regulatory genes, 52 interact with themselves or with other regulatory genes through 72 additional links (see Web Table A online). A transcriptional regulatory network can thus be represented as a graph where vertices are genes and directed edges denote activating or repressing effects on transcription. The graph of these 52 'interregulatory' gene...

Genome-wide system analysis reveals stable yet flexible network dynamics in yeast

“…Figure 1a shows that the gene network has a significant (RAND) broad out-degree distribution, as previously has been observed also in protein and metabolic networks [1]. The distribution does not follow a power-law, as many previously published biological networks do (for example [23]). However, there is no theoretical justification why all networks should have this property, and there are also many examples of when this is not the case (for example [24] …”

Gene regulatory network growth by duplication

“…A number of topological features are common to the gene regulatory networks in E. coli and yeast 2,3 . A key common feature is that the number of target genes per transcription factor roughly obeys a power law, which is typical of 'scale-free' networks 19 (Fig.…”

“…2a), or it may recognize a new binding site upstream of some other target gene(s). Investigation of the known network in both organisms 2,3,7 showed that duplication of transcription factor genes followed by inheritance of interaction has contributed considerably to the growth of the regulatory network: more than two-thirds of E. coli (77%) and yeast (69%) transcription factors have at least one interaction in common with their duplicates (Table 1). This accounts for 128 interactions (10%) in E. coli and 188 interactions (22%) in yeast ( Fig.…”

Genes regulated by caloric restriction have unique roles within transcriptional networks

“…Network approaches to the analysis of DNA microarray data can reveal interesting properties of genes that are not discernable through standard differential expression analysis (Guelzim et al, 2002;Alon, 2003;Bray, 2003;Ge et al, 2003;Ferrarini et al, 2005). Gene expression is regulated by individual transcription factors, or combinations of such factors, that bind to DNA sequences to promote or inhibit expression of target genes.…”

“…In either case, connected genes are often elements of the same biological pathway (Genter et al, 2003;Zhang et al, 2004;Wolfe et al, 2005;Ulitsky and Shamir, 2007), and such genes may participate in similar biological processes (but see Allocco et al, 2004 andYeung et al, 2004a). An interesting property of transcription networks, indeed most biological networks, is a "scale-free" topology, where few hub genes are highly connected to many other genes, with numerous weakly-connected genes located peripherally to highly-connected hubs (Guelzim et al, 2002;Siegal et al, 2007). The density of transcriptional networks, therefore, is distributed in non-random fashion, with certain compact regions corresponding to large gene groups with similar expression patterns.…”