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Comparing protein interaction networks via a graph match-and-split algorithm.

Manikandan Narayanan1, Richard M Karp

  • 1Computer Science Division, University of California, Berkeley, California 94720, USA. nmani@cs.berkeley.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|September 7, 2007
PubMed
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We developed a novel algorithm to compare protein networks across species, identifying conserved functional modules. This method offers provable efficiency and correctness for detecting shared biological pathways.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Protein interaction networks are crucial for understanding cellular functions.
  • Identifying conserved protein modules across species aids in inferring biological functions and evolutionary relationships.
  • Existing network comparison methods often lack provable guarantees on correctness and efficiency.

Purpose of the Study:

  • To present a novel algorithm for comparing protein interaction networks between species.
  • To detect functionally similar (conserved) protein modules.
  • To provide a method with provable correctness and efficiency.

Main Methods:

  • Developed a graph-matching algorithm to identify conserved subgraphs (protein modules).
  • Applied the method to pairwise comparisons of yeast, human, fruit fly, and nematode worm protein networks.

Related Experiment Videos

  • Utilized a clustering heuristic and a lenient criterion based on connectedness and matching edges.

    • Main Results:

    • The method demonstrated competitive performance against existing network comparison techniques.
    • It outperformed a popular single-species clustering method under specific conditions.
    • Detected conserved modules showed biological relevance and facilitated cross-species annotation transfer.

    Conclusions:

    • The developed network comparison method is effective for identifying conserved protein modules.
    • It offers advantages in correctness and efficiency over previous approaches.
    • This approach is valuable for functional annotation transfer and understanding cross-species biological conservation.