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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

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A unified framework of mutual influence between two pathogens in multiplex networks

Yanping Zhao1, Muhua Zheng1, Zonghua Liu1

  • 1Department of Physics, East China Normal University, Shanghai 200062, China.

Chaos (Woodbury, N.Y.)
|January 3, 2015
PubMed

Insights

This study introduces a new framework for understanding how different pathogens interact in complex networks. It reveals that pathogen interactions, including mutual enhancement or suppression, depend on a novel "state-dependent infectious rate."

Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Pathogen interactions and their epidemic influences in multiplex networks are widely observed.
  • Existing frameworks lack a unified approach to describe diverse dynamic outcomes of pathogen interplay.
  • Previous models often assume a constant infectious rate, limiting the scope of interaction dynamics.

Purpose of the Study:

  • To develop a unified framework for understanding pathogen interactions in multiplex networks.
  • To introduce and investigate the concept of a state-dependent infectious rate.
  • To explain diverse interaction outcomes, including mutual enhancement, suppression, and complex temporal effects.

Main Methods:

  • Development of a theoretical model incorporating a two-layered multiplex network.
  • Introduction of a state-dependent infectious rate, varying based on pathogen states.
  • Numerical simulations and theoretical analysis to explore parameter spaces and predict outcomes.

Main Results:

  • Demonstrated that a state-dependent infectious rate can unify various pathogen interaction dynamics.
  • Identified parameter ranges corresponding to mutual enhancement, mutual suppression, and complex feedback loops.
  • Showcased scenarios where initial cooperation leads to final suppression, and vice versa.

Conclusions:

  • The state-dependent infectious rate provides a novel and unified perspective on pathogen co-epidemics.
  • This framework accurately captures complex interdependencies and dynamic shifts in pathogen interactions.
  • The findings offer a more comprehensive understanding of disease dynamics in interconnected systems.

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