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In silico microbial community model simulating oral multi-species biofilms linked to periodontitis

Computational models can be powerful tools to simulate and understand responses of biological systems. In this project, we construct such an in silico model to describe and predict the dynamicsof oral biofilms associated with periodontitis. Periodontitis is a chronic inflammatory and destructive disease of the tooth supporting tissue, and often associates with a disturbance of the
tooth biofilm causing outgrowth of pathogens. Previous research has identified prebiotic compounds which can selectively stimulate beneficial bacteria and keep the community in a
healthy state. To understand the action radius and effectiveness of identified prebiotics, a biofilm community model will be developed that describes the mechanisms of prebiotics and can be usedto computationally analyse and design of prebiotic treatments. This model will also be able to predict the effect of probiotics (beneficial bacteria) and to simulate shifts from a healthy to a
disease state. Genome scale metabolic networks are used to describe the metabolic capabilities of each individual strain. These GSMNs are integrated in a community model including metabolic
interactions as well as diffusion, spatial distribution and shear stress. This type of in silico model for the oral biofilm will be the first of its kind.

Date:1 Oct 2017 →  13 Jan 2020
Keywords:genome-scale metabolic network modelling, (dynamic) flux balance analysis, spatiotemporal community modelling for biofilms
Disciplines:Catalysis and reacting systems engineering, Chemical product design and formulation, General chemical and biochemical engineering, Process engineering, Separation and membrane technologies, Transport phenomena, Other (bio)chemical engineering
Project type:PhD project