This is an accompanying webpage to the paper
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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Abstract
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.