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Activity motifs reveal principles of timing in transcription
control of the yeast metabolic network

Nature Biotechnology, 26, 1251-1259 (26 Oct 2008)
Gal Chechik, Eugene Oh, Oliver Rando, Jonathan Weissman, Aviv Regev, Daphne Koller

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Significant insight about biological networks arises from the study of network motifs small wiring patterns that are overly abundant in the network. However, wiring patterns, like a street map, only reflect the set of potential routes within a cellular network, but not when and how they are used within different cellular processes. Here, we introduce activity motifs, which, like traffic flow, reflect dynamic patterns that are abundant relative to the given network, and use them to study the timing of transcriptional regulation in Saccharomyces cerevisiae metabolism. Specific timing activity motifs, reflecting ordered transcription, are enriched in cellular responses to changing conditions: Linear pathways are enriched for forward activation patterns to produce metabolic compounds efficiently; backward activation to rapidly initiate the production of a critical substrate; and backward shutoff to rapidly stop production of a detrimental product. Branching pathways are enriched for synchronized activation of dependent co-production. We validate our model by measuring protein abundance over a time course, showing that our inferred mRNA timing motifs also occur at the protein level. We also find binding activity motifs, where the genes in a linear chain have ordered binding strength to a particular transcription factor; these binding activity motifs overlap significantly with the timing activity motifs, suggesting a specific biochemical mechanism for ordered transcription. The results show that finely-timed transcriptional regulation is abundant in the yeast metabolic network, and is likely to play a role in its adaptation to new environmental conditions. More generally, the framework of activity motifs is applicable for analyzing a variety of biological networks and functional data, and may be useful in elucidating a broad range of cellular functions.