The Use of Interdisciplinary Approaches to Understand the Biology of Campylobacter jejuni

Paulina A. Dzianach, Francisco J. Pérez-Reche*, Norval J.C. Strachan, Ken J. Forbes, Gary A. Dykes

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)
2 Downloads (Pure)

Abstract

Campylobacter jejuni is a bacterial pathogen recognised as a major cause of foodborne illness worldwide. While Campylobacter jejuni generally does not grow outside its host, it can survive outside of the host long enough to pose a health concern. This review presents an up-to-date description and evaluation of biological, mathematical, and statistical approaches used to understand the behaviour of this foodborne pathogen and suggests future avenues which can be explored. Specifically, the incorporation of mathematical modelling may aid the understanding of C. jejuni biofilm formation both outside and inside the host. Predictive studies may be improved by the introduction of more standardised protocols for assessments of disinfection methods and by assessment of novel physical disinfection strategies as well as assessment of the efficiency of plant extracts on C. jejuni eradication. A full description of the metabolic pathways of C. jejuni, which is needed for the successful application of metabolic models, is yet to be achieved. Finally, a shift from animal models (except for those that are a source of human campylobacteriosis) to human-specific data may be made possible due to recent technological advancements, and this may lead to more accurate predictions of human infections.

Original languageEnglish
Article number2498
Number of pages17
JournalMicroorganisms
Volume10
Issue number12
DOIs
Publication statusPublished - 16 Dec 2022

Bibliographical note

Funding Information:
This work was supported by a scholarship grant from the University of Aberdeen and Curtin University.

Keywords

  • biological models
  • foodborne pathogens
  • infection control
  • mathematical models
  • multiscale descriptions
  • statistical models

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