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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

Inferring social network structure from bacterial sequence data.

Mateusz M Pluciński¹, Richard Starfield, Rodrigo P P Almeida

¹Department of Environmental Science, Policy and Management, University of California, Berkeley, California, United States of America. mateusz@berkeley.edu

|August 11, 2011

Summary

This summary is machine-generated.

Analyzing commensal bacteria DNA using multilocus sequence typing (MLST) can reveal host contact network structures. This method infers population connectivity and individual proximity from bacterial genetic distances.

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Last Updated: May 14, 2026

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

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Published on: November 12, 2012

Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry

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Published on: November 12, 2012

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Microbiology
Network Science
Epidemiology

Background:

Traditionally, DNA sequencing infers pathogen transmission during outbreaks.
Commensal bacteria, present in healthy individuals, offer an alternative data source.
Host contact networks are crucial for understanding disease spread and social structures.

Purpose of the Study:

To investigate if DNA sequence data from commensal bacteria can infer host contact network properties.
To determine if multilocus sequence typing (MLST) data from commensals can estimate network structure parameters.
To explore the correlation between genetic distances and network distances (degrees of separation).

Main Methods:

Simulated multilocus DNA sequence data from small-world networks.
Analysis using multilocus sequence typing (MLST) schemes.
Statistical estimation of network parameters and correlation analysis.

Main Results:

MLST data from commensal bacteria robustly estimate network structure parameters, like the small-world parameter.
Genetic distances between bacterial isolates correlate significantly with pairwise distances in the host contact network.
Inferred network properties provide insights into population connectivity.

Conclusions:

Commensal bacteria DNA sequencing is a viable tool for inferring host contact networks.
This approach can quantify population structure and individual relatedness.
Potential applications include testing infectious origins of diseases with unknown etiology.