Computational Network Inference for Bacterial Interactomics

Katherine James, Jose Muñoz-Muñoz

    Research output: Contribution to journalReview articlepeer-review

    10 Citations (Scopus)
    70 Downloads (Pure)

    Abstract

    Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.
    Original languageEnglish
    Article numbere01456-21
    Pages (from-to)1-15
    Number of pages15
    JournalmSystems
    Volume7
    Issue number2
    Early online date30 Mar 2022
    DOIs
    Publication statusPublished - 26 Apr 2022

    Keywords

    • interactome
    • interologs
    • data integration
    • cellular network analysis
    • systems biology

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