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Published May 2015 | Published + Supplemental Material
Journal Article Open

Experimental and Computational Analysis of a Large Protein Network That Controls Fat Storage Reveals the Design Principles of a Signaling Network


An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates "small-world" networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein's position within a module and to the module's relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.

Additional Information

© 2015 Al-Anzi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: October 23, 2014; Accepted: April 2, 2015; Published: May 28, 2015. We thank Robert Oania, Christopher Frick, Mark Budde, Erich Schwarz, and Eric Forgoston for helpful discussions and comments on the manuscript. Author Contributions: Conceived and designed the experiments: BAA KZ. Performed the experiments: BAA PA CO SG NO. Analyzed the data: BAA KZ CO SG NO. Contributed reagents/materials/analysis tools: BAA CO SG NO. Wrote the paper: BAA KZ CO NO. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: This work was funded by a National Institutes of Health R21 grant to KZ, NS083874. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

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August 20, 2023
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