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Related Experiment Video

Updated: Sep 28, 2025

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Computational Network Inference for Bacterial Interactomics.

Katherine James¹, Jose Muñoz-Muñoz¹

¹Northumbria Universitygrid.42629.3b, Faculty of Health and Life Sciences, Department of Applied Sciences, Newcastle upon Tyne, United Kingdom.

|March 30, 2022

Summary

This summary is machine-generated.

Computational methods predict protein-protein interactions (PPIs) for species lacking experimental data. This review focuses on microbial interactome inference, highlighting techniques and applications for bacterial systems biology.

Keywords:

cellular network analysis data integration interactome interologs systems biology

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Last Updated: Sep 28, 2025

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

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Published on: November 12, 2012

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Experimental characterization of protein-protein interactions (PPIs) is challenging for many species.
Computational PPI prediction methods leverage cross-species data, but microbial applications lag behind eukaryotes.
Bacterial interactomes can be inferred using established computational techniques, with some methods better suited for bacterial genomes.

Purpose of the Study:

To review current computational network inference and analysis techniques for microbial interactomes.
To summarize the application of computationally-predicted microbial interactomes in systems-level analyses.
To bridge the gap in microbial interactome research compared to eukaryotes.

Main Methods:

Review of existing computational PPI prediction methodologies.
Adaptation and application of eukaryotic PPI inference techniques to bacterial genomes.
Network analysis of computationally-derived microbial interactomes.

Main Results:

Computational methods enable the prediction of protein-protein interaction networks in species lacking experimental data.
Several computational approaches are well-suited for inferring bacterial interactomes.
Predicted microbial interactomes facilitate systems-level biological analyses.

Conclusions:

Computational prediction of protein-protein interactions is crucial for understanding biological systems, especially in microbes.
The review highlights the potential and current state of microbial interactome inference.
Inferred microbial interactomes offer valuable insights for species without extensive experimental interaction data.