Modelling dynamics between free‐living amoebae and bacteria

Author:

Ali Marwa1ORCID,Rice Christopher A.1234ORCID,Byrne Andrew W.5ORCID,Paré Philip E.6ORCID,Beauvais Wendy13

Affiliation:

1. Comparative Pathobiology Department, Purdue Veterinary Medicine Purdue University West Lafayette Indiana USA

2. Purdue Institute for Drug Discovery (PIDD) Purdue University West Lafayette Indiana USA

3. Purdue Institute of Inflammation, Immunology and Infectious Disease (PI4D) Purdue University West Lafayette Indiana USA

4. Regenstrief Center for Healthcare Engineering (RHCE) Purdue University West Lafayette Indiana USA

5. One Health Scientific Support Unit, National Disease Control Centre, Agriculture House Dublin Ireland

6. Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

Abstract

AbstractFree‐living amoebae (FLA) serve as hosts for a variety of endosymbionts, which are microorganisms that reside and multiply within the FLA. Some of these endosymbionts pose a pathogenic threat to humans, animals, or both. The symbiotic relationship with FLA not only offers these microorganisms protection but also enhances their survival outside their hosts and assists in their dispersal across diverse habitats, thereby escalating disease transmission. This review is intended to offer an exhaustive overview of the existing mathematical models that have been applied to understand the dynamics of FLA, especially concerning their interactions with bacteria. An extensive literature review was conducted across Google Scholar, PubMed, and Scopus databases to identify mathematical models that describe the dynamics of interactions between FLA and bacteria, as published in peer‐reviewed scientific journals. The literature search revealed several FLA–bacteria model systems, including Pseudomonas aeruginosa, Pasteurella multocida, and Legionella spp. Although the published mathematical models account for significant system dynamics such as predator–prey relationships and non‐linear growth rates, they generally overlook spatial and temporal heterogeneity in environmental conditions, such as temperature, and population diversity. Future mathematical models will need to incorporate these factors to enhance our understanding of FLA–bacteria dynamics and to provide valuable insights for future risk assessment and disease control measures.

Funder

National Science Foundation

Publisher

Wiley

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