title: Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.
abstract: 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.
authors.affiliation: Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.
authors.forename: Bader
authors.initials: B
authors.lastname: Al-Anzi
authors.affiliation: Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.
authors.forename: Patrick
authors.initials: P
authors.lastname: Arpp
authors.affiliation: Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Sherif
authors.initials: S
authors.lastname: Gerges
authors.affiliation: Division of Physics, Mathematics, and Astronomy, California Institute of Technology, Pasadena, California, United States of America.
authors.forename: Christopher
authors.initials: C
authors.lastname: Ormerod
authors.affiliation: Control and Dynamical Systems Option, Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, California, United States of America.
authors.forename: Noah
authors.initials: N
authors.lastname: Olsman
authors.affiliation: Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.
authors.forename: Kai
authors.initials: K
authors.lastname: Zinn
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D005227:Fatty Acids / Q000737:chemistry; D017353:Gene Deletion; D020935:MAP Kinase Signaling System; D008957:Models, Genetic; D008962:Models, Theoretical; D009154:Mutation; D010641:Phenotype; D011506:Proteins / Q000737:chemistry*; D020543:Proteome; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000737:chemistry*; D020123:Sirolimus / Q000737:chemistry; D012984:Software; D049490:Systems Biology
chemical_list: D005227:Fatty Acids; D011506:Proteins; D020543:Proteome; D020123:Sirolimus