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A mechanistic Individual-based Model of microbial communities

Research output: Contribution to journalJournal articlepeer-review

  • Pahala Gedara Jayathilake
  • Prashant Gupta
  • Bowen Li
  • Curtis Madsen
  • Oluwole Oyebamiji
  • Rebeca González-Cabaleiro
  • Steve Rushton
  • Ben Bridgens
  • David Swailes
  • Ben Allen
  • A. Stephen McGough
  • Paolo Zuliani
  • Irina Dana Ofiteru
  • Darren Wilkinson
  • Jinju Chen
  • Tom Curtis
<mark>Journal publication date</mark>1/08/2017
<mark>Journal</mark>PLoS ONE
Publication StatusPublished
<mark>Original language</mark>English


Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. How-ever, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly com-bine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for " bottom up " prediction of the emer-gent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacte-ria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria inter-action is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentra-tion, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup.