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Cited by 266

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How the global structure of protein interaction networks evolves

Open access
Wagner, Andreas·2003·Proceedings of the Royal Society of London. Series B: Biological Sciences

Abstract

Two processes can influence the evolution of protein interaction networks: addition and elimination of interactions between proteins, and gene duplications increasing the number of proteins and interactions. The rates of these processes can be estimated from available Saccharomyces cerevisiae genome data and are sufficiently high to affect network structure on short time-scales. For instance, more than 100 interactions may be added to the yeast network every million years, a fraction of which adds previously unconnected proteins to the network. Highly connected proteins show a greater rate of interaction turnover than proteins with few interactions. From these observations one can explain (without natural selection on global network structure) the evolutionary sustenance of the most prominent network feature, the distribution of the frequency P(d) of proteins with d neighbours, which is broad-tailed and consistent with a power law, that is: P(d) proportional, variant d (-gamma).

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Metadata

Authors
Wagner, A.
Venue
Proceedings of the Royal Society of London. Series B: Biological Sciences
Published
2003-03-07
Volume
270
Issue
1514
Pages
457-466
ISSN
  • 0080-4649 (ISSN-L)
  • 0962-8452
  • 1471-2954
OpenAlex
W2117526623
Cited by
266
References
7
FWCI
7.51
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: How the global structure of protein interaction networks evolves. https://app.cochat.ai/papers/10.1098/rspb.2002.2269
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Wagner, A. (2003). How the global structure of protein interaction networks evolves. Proceedings of the Royal Society of London. Series B: Biological Sciences. https://doi.org/10.1098/rspb.2002.2269

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.