Maintenance of oral health is essential. It is known that the microbiome, host response, and human behaviours (e.g. tooth brushing) play an essential role in maintaining oral health. In addition, the oral microbiome is a highly complex system, with considerable diversity between individual people. It is also essential that consumer products that contact the oral microbiome do not have unexpected detrimental effects on it.
The aim of this project is to develop a mathematical model to characterise oral microbiome resilience, and to use the model to identify factors predictive of dysbiosis/stability of the oral microbiome. Such a description would be key to determine functions of the microbiome which need to be protected to ensure consumer health as part of microbiological risk assessments. The student will develop dynamical mathematical models for competing microbial populations in the oral microbiome, based upon existing models developed at the University of Nottingham, as well as literature models. The models will include dynamics of growth, death, physical removal, and competition of microbial populations. The model will be fitted to metagenomics and metatranscriptomics data from a longitudinal clinical study of experimental gingivitis being carried out at Newcastle University, using Bayesian model fitting techniques. The model will then be subject to local and global sensitivity analyses and counterfactual simulations, in order to identify factors in the model predictive of health/dysbiosis. These will be used to inform risk analysis pipelines for Unilever.
This project is offered in partnership with Unilever and Newcastle University. In addition to support and training offered by the University of Nottingham, the student will benefit from short (1-2 week) and long (3 month) placements at Unilever, as well as a placement at Newcastle University. At Unilever, the student will work with bespoke bioinformatics pipelines developed at Unilever and will be supported by experts both in bioinformatics and oral microbiology from the R&D team.
The student will also develop transferrable professional business skills in a corporate environment. At Newcastle, the student will work in Nick Jakubovics’s laboratory to develop their understanding of how the metagenomic and metatranscriptomic data are generated. The project will suit a student with a relevant STEM degree and strong quantitative background, that would include skills with differential equations and statistics, as well as programming skills in a relevant language, e.g. Python or R. Knowledge of biology would be an advantage, as would a relevant Masters degree, e.g. in Bioinformatics or Systems Biology.
Please submit your application via our website: https://www.nottingham.ac.uk/bbdtp/apply/apply-online.aspx’
Closing date: Noon, Wednesday 24th August 2022.