Poster Presentation Australian Society for Microbiology Annual Scientific Meeting 2018

Computational Model of Campylobacter jejuni Biofilms (#230)

Paulina A Dzianach 1 2 , Gary A Dykes 2 , Ken J Forbes 3 , Norval JC Strachan 1 , Francisco J Pérez-Reche 1
  1. School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, Aberdeenshire, Scotland
  2. School of Public Health, Curtin University, Bentley, Western Australia, Australia
  3. School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, Aberdeenshire, Scotland

 The ability of bacteria to attach and grow on surfaces poses a significant challenge to both industry and human health. Campylobacter jejuni, an enteric human pathogenic species capable of forming biofilms, is well known for causing foodborne illness around the globe. The complexity of the interaction of bacteria with surfaces to form biofilms lends itself well to study using mathematical models. This study aims to develop a mathematical model to predict the environmental and biological conditions under which C. jejuni biofilms form. The model is a stochastic cellular automaton which was initially developed by using data on Campylobacter jejuni biofilms from the literature. Nutrient uptake, growth, autolysis, cell deactivation and extracellular matrix development are simulated as random processes. In parallel with microbial processes, both growth limiting nutrients and oxygen concentration fields are used to simulate the effect of availability of specific compounds on the microbial community. The developed model predicts the emergence of dense biofilm structures at high nutrient levels when oxygen levels allow growth. Conversely, fractal-like structures appear to develop whenever nutrients become limited. C. jejuni is a microaerophilic species and the model assumes that high oxygen levels inhibit biofilm development. However, there is a threshold value of oxygen concentration for each set of parameters at which there is a specific probability of biofilm forming.  This suggests that the ability of bacteria to form a biofilm at the threshold depends on a range of random events, such as the way the biofilm matrix is arranged, or the order of subsequent events, such as cell division or cell autolysis. At present, the parameters used to run the simulations are based on reasonable assumptions only. The next stage of the project is to measure parameters of the model experimentally to obtain better predictions. It is hoped that this new approach to the study of C. jejuni biofilms will ultimately help control this pathogen.