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Modelling dynamics between free-living amoebae and bacteria.

Marwa Ali1, Christopher A Rice1,2,3,4, Andrew W Byrne5

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Summary
This summary is machine-generated.

Free-living amoebae (FLA) host pathogenic bacteria, increasing disease transmission. Mathematical models of FLA-bacteria dynamics often neglect environmental factors, limiting accurate risk assessment and control strategies.

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Area of Science:

  • Microbiology
  • Mathematical Biology
  • Ecology

Background:

  • Free-living amoebae (FLA) are protozoa that host various endosymbionts, including pathogenic bacteria.
  • These symbiotic relationships enhance bacterial survival, dispersal, and potential disease transmission to humans and animals.
  • Understanding FLA-bacteria interactions is crucial for public health and disease control.

Purpose of the Study:

  • To provide a comprehensive review of mathematical models analyzing free-living amoebae-bacteria dynamics.
  • To identify the strengths and limitations of current models in representing these interactions.
  • To highlight areas for future model development.

Main Methods:

  • Extensive literature search across Google Scholar, PubMed, and Scopus databases.
  • Identification and analysis of peer-reviewed scientific articles on mathematical models of FLA-bacteria interactions.
  • Synthesis of findings regarding model dynamics, included species, and overlooked factors.

Main Results:

  • Several FLA-bacteria model systems were identified, including interactions with Pseudomonas aeruginosa, Pasteurella multocida, and Legionella spp.
  • Existing models capture key dynamics like predator-prey relationships and non-linear growth.
  • A significant limitation is the general neglect of spatial-temporal environmental heterogeneity (e.g., temperature) and population diversity.

Conclusions:

  • Current mathematical models offer valuable insights into FLA-bacteria dynamics but require enhancement.
  • Incorporating environmental heterogeneity and population diversity is essential for more accurate predictions.
  • Improved models will bolster risk assessment and inform effective disease control measures.