Genomic analysis of regulatory network dynamics reveals large topological changes

Abstract

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here--particularly the large-scale topological changes and hub transience--will apply to other biological networks, including complex sub-systems in higher eukaryotes.

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

“…To further analyze the network statistics we calculate all three and four gene network motifs in the network graph by applying the m-finder algorithm 3 . All results presented are statistically significant, which we here assure by only considering node-sets (i.e., motifs) found at least 20 times in the network and having large Z-scores (Z(RAND)>5 and Z(REWIRED)>2 [8,16]). 2 If there are feedback loops with an even number of negative regulations in the network, i.e., effectively self-activating sub-systems, this argument is weakened.…”

“…First, we do not consider the signs of the interactions, and recover the previously defined motifs described in Yeast regulatory networks [8,16]. 4 Feed-forward loops (FFL), bi-parallel and bi-fan motifs are overrepresented (figure 2a).…”

“…This data is often analyzed by clustering over different experiments of wholegenome expression profiles, and that technique has provided important insights into gene function [2]. However, clustering alone cannot resolve gene interactions, and progress in network identification algorithms has revealed aspects of the static wiring of gene networks [3][4][5][6][7][8][9][10][11]. A recent study by Luscombe and colleagues [8] provided a first step towards an understanding of network dynamics by describing when different sub-networks are active during different cellular conditions in Yeast.…”

“…However, clustering alone cannot resolve gene interactions, and progress in network identification algorithms has revealed aspects of the static wiring of gene networks [3][4][5][6][7][8][9][10][11]. A recent study by Luscombe and colleagues [8] provided a first step towards an understanding of network dynamics by describing when different sub-networks are active during different cellular conditions in Yeast. A general review of various methods for uncovering the structure of gene regulatory networks from experimental data can be found in [12] and of graph theoretical tools for the analysis in [13,14].…”

Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity

“…Then, the degree-degree correlation k nn (k) increases with degree k. [27]. For larger ξ ∈ [3,15], assortative coefficients r monotonically decrease (upward) with increasing m/a. Figure 7 (C) shows the projections of the plot on the ξ-r plane for Fig.…”