Use this URL to cite or link to this record in EThOS:
Title: Stochastic modelling of repeat-mediated phase variation in Campylobacter jejuni
Author: Howitt, Ryan
ISNI:       0000 0004 7233 8836
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Restricted access.
Access from Institution:
It is of interest to determine how populations of bacteria whose genes exhibit an ON/OFF switching property (phase variation) evolve over time from an initial population. By statistical analysis of two in vitro experimental Campylobacter jejuni datasets containing 28 genes assumed to be phase variable, we find evidence of small networks of genes which exhibit dependent evolutionary behaviour. This violates the assumption that the genes in these datasets do not interact with one another in the way they mutate during the division of cells, motivating the development of a model which attempts to explain evolution of such genes with factors other than mutation alone. We show that discrete probability distributions at observation times can be estimated by utilising two stochastic models. One model provides an explanation with mutation rates in genes, resembling a Markov chain under the assumption of having a near infinite population size. The second provides an explanation with both mutation and natural selection. However, the addition of selection parameters makes this model resemble a non-linear Markov process, which makes further analysis less straight-forward. An algorithm is constructed to test whether the mutation-only model can sufficiently explain evolution of single phase variable genes, using distributions and mutation rates from data as examples. This algorithm shows that applying this model to the same phase variable genes believed to show dependent evolutionary behaviour is inadequate. We use Approximate Bayesian Computation to estimate selection parameters for the mutation with selection model, whereby inference is derived from samples drawn from an approximation of the joint posterior distribution of the model parameters. We illustrate this method on an example of three genes which show evidence of dependent evolutionary behaviour from our two datasets.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA276 Mathematical statistics