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Updated: Jun 14, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

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

Published on: November 12, 2012

Interacting epidemics on overlay networks.

Sebastian Funk1, Vincent A A Jansen

  • 1School of Biological Sciences, Royal Holloway, University of London, Egham, Surrey TW20 0EX, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|April 7, 2010
PubMed
Summary
This summary is machine-generated.

Understanding pathogen spread on networks is complex. Positive correlation between network structures enhances protection against subsequent disease outbreaks, especially with heterogeneous networks.

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Viral Concentration Determination Through Plaque Assays: Using Traditional and Novel Overlay Systems
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Last Updated: Jun 14, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

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

Published on: November 12, 2012

Viral Concentration Determination Through Plaque Assays: Using Traditional and Novel Overlay Systems
09:28

Viral Concentration Determination Through Plaque Assays: Using Traditional and Novel Overlay Systems

Published on: November 4, 2014

Area of Science:

  • Complex Systems
  • Epidemiology
  • Network Science

Background:

  • Interactions between multiple pathogens on networks pose theoretical challenges.
  • Understanding these dynamics is crucial for predicting and controlling disease spread.

Purpose of the Study:

  • To investigate the impact of network structure on the interaction between two spreading processes.
  • To analyze how degree correlations and heterogeneity influence epidemic dynamics and protection.

Main Methods:

  • Studied bond percolation of two processes on overlay networks.
  • Analyzed arbitrary joint degree distributions and varying edge overlap.
  • Investigated partial and mutual immunity scenarios.

Main Results:

  • Positive degree correlation between networks enhances protection against a second pathogen.
  • Increased network heterogeneity amplifies protection when degrees are positively correlated.
  • Uncorrelated or negatively correlated degrees reduce the protective effect of the first pathogen.

Conclusions:

  • Network structure significantly impacts multi-pathogen interactions and disease control.
  • Degree correlations and heterogeneity are key factors in determining epidemic outcomes and coexistence thresholds.