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.