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A survey of computational methods in protein–protein interaction networks

  • S.I.: Computational Biomedicine
  • Published:
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Abstract

Protein–protein interaction networks are mathematical constructs where every protein is represented as a node, with an edge signaling that two proteins interact. These constructs have enabled a series of graph theoretic computational methods in the analysis of how cell life works. Such methods have found diverse applications from helping create more reliable interaction data, to identifying new protein complexes and predict their functionalities, and investigating the minimum requirements for cell life through protein essentiality. Our goal with this survey is to provide an overview of the research in the area from a network analysis perspective. In this work, we provide a brief introduction to protein–protein interaction networks, followed by the methods that we currently have to obtain such interactions and the databases they can be found at. Then, we proceed to discuss the network properties of protein–protein interaction networks and how they can be exploited to identify protein complexes and functional modules, as well as help classify proteins as essential. We finish this survey with a full bibliography on work in protein–protein interactions that could be of interest to operations research and computational science academicians and practitioners.

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Acknowledgements

Chrysafis Vogiatzis was supported by ND EPSCoR NSF #1355466 during his tenure at North Dakota State University. The authors would like to thank the editors and the anonymous reviewers for their comments that helped improve the manuscript.

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  1. Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND, USA

    Saeid Rasti

  2. Department of Industrial and Systems Engineering, North Carolina A&T State University, Greensboro, NC, USA

    Chrysafis Vogiatzis

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ND EPSCoR NSF 1355466.

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Rasti, S., Vogiatzis, C. A survey of computational methods in protein–protein interaction networks. Ann Oper Res 276, 35–87 (2019). https://doi.org/10.1007/s10479-018-2956-2

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