Specificity and Stability in Topology of Protein Networks
Top Cited Papers
- 3 May 2002
- journal article
- other
- Published by American Association for the Advancement of Science (AAAS) in Science
- Vol. 296 (5569), 910-913
- https://doi.org/10.1126/science.1065103
Abstract
Molecular networks guide the biochemistry of a living cell on multiple levels: Its metabolic and signaling pathways are shaped by the network of interacting proteins, whose production, in turn, is controlled by the genetic regulatory network. To address topological properties of these two networks, we quantified correlations between connectivities of interacting nodes and compared them to a null model of a network, in which all links were randomly rewired. We found that for both interaction and regulatory networks, links between highly connected proteins are systematically suppressed, whereas those between a highly connected and low-connected pairs of proteins are favored. This effect decreases the likelihood of cross talk between different functional modules of the cell and increases the overall robustness of a network by localizing effects of deleterious perturbations.Keywords
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This publication has 14 references indexed in Scilit:
- Functional organization of the yeast proteome by systematic analysis of protein complexesNature, 2002
- Dynamical and Correlation Properties of the InternetPhysical Review Letters, 2001
- The Yeast Protein Interaction Network Evolves Rapidly and Contains Few Redundant Duplicate GenesMolecular Biology and Evolution, 2001
- Lethality and centrality in protein networksNature, 2001
- A comprehensive two-hybrid analysis to explore the yeast protein interactomeProceedings of the National Academy of Sciences, 2001
- YPDTM, PombePDTM and WormPDTM: model organism volumes of the BioKnowledgeTM Library, an integrated resource for protein informationNucleic Acids Research, 2001
- Surfing the p53 networkNature, 2000
- The small world of metabolismNature Biotechnology, 2000
- Error and attack tolerance of complex networksNature, 2000
- Graph structure in the WebComputer Networks, 2000