Recent computational and experimental work suggests that functional modules underlie much of cellular physiology and are a useful unit of cellular organization from the perspective of systems biology. Because interactions among modules can give rise to higher-level properties that are essential to cellular function, a complete knowledge of these interactions is necessary for future work in systems biology, including in silico modeling and metabolic engineering. Here we present a computational method for the systematic identification and analysis of functional modules whose activity is coordinated at the level of transcription. We applied this method, Search for Pairwise Interactions (SPIN), to obtain a global view of functional module connectivity in Saccharomyces cerevisiae and to provide insight into the biological mechanisms underlying this coordination. We also examined this global network at higher resolution to obtain detailed information about the interactions of particular module pairs. For instance, our results reveal possible transcriptional coordination of glycolysis and lipid metabolism by the transcription factor Gcr1p, and further suggest that glycolysis and phosphoinositide signaling may regulate each other reciprocally.[Supplemental material is available online at www.genome.org.]The phenotype of a unicellular organism is determined by an integrated network of genes, proteins, and metabolites that participate in reciprocal regulatory relationships. Creating a quantitative description of this network-a goal that has recently engendered the dedicated discipline of systems biology-is essential to understanding, predicting, and manipulating cellular behavior. A first step toward this goal is deciphering the connectivity of the network, that is, the pattern of interactions among its components. Given the complexity of this undertaking, the integrated network is often treated as a group of superimposed subnetworks, including the gene regulatory, protein, and metabolic networks. A corollary task is to determine whether the network's topology reflects its organizational principles. Using biological networks with relatively well-characterized connectivity, quantitative analyses of network topology have revealed that these networks are modular-they can be clustered into nodes that are more densely connected to each other than to nodes in other clusters (Ravasz et al.