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How the global structure of protein interaction networks evolves.

Andreas Wagner1

  • 1Department of Biology, University of New Mexico, 167A Castetter Hall, Albuquerque, NM 817131-1091, USA. wagnera@unm.edu

Proceedings. Biological Sciences
|March 19, 2003
PubMed
Summary

Protein interaction networks evolve rapidly through interaction changes and gene duplications. These dynamic processes explain the sustained power-law distribution of protein connections in yeast, without needing selection on network structure.

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Area of Science:

  • Evolutionary biology
  • Systems biology
  • Bioinformatics

Background:

  • Protein interaction networks are crucial for cellular functions.
  • Network evolution is shaped by interaction dynamics and gene duplication events.
  • Understanding these evolutionary processes is key to comprehending biological complexity.

Purpose of the Study:

  • To investigate the evolutionary dynamics of protein interaction networks.
  • To determine if observed network structures can arise from simple evolutionary processes.
  • To explain the prevalence of power-law distributions in protein interaction networks.

Main Methods:

  • Analysis of Saccharomyces cerevisiae genome data.
  • Estimation of interaction addition/elimination and gene duplication rates.

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  • Modeling of network evolution based on estimated rates.

    • Main Results:

    • High rates of protein interaction turnover and gene duplication in yeast.
    • Over 100 interactions can be added to the yeast network per million years.
    • Highly connected proteins exhibit faster interaction turnover.
    • These processes naturally generate the observed power-law degree distribution.

    Conclusions:

    • The power-law distribution of protein interaction networks is a consequence of evolutionary dynamics, not necessarily global network selection.
    • Simple processes of interaction turnover and gene duplication are sufficient to explain key network features.
    • This provides a parsimonious explanation for the structure of biological networks.