Project: Optimal design of competitive mixture experiments
Competitive mixture experiments consist of infecting a host (in this case, a ferret), with different proportions of virus subtypes with nominally differing fitness levels. The ferret is then co-housed with a susceptible (uninfected) ferret, and the abundance of the viral-subtypes is quantified in each host over time. The aim of these experiments is to determine the absolute and relative fitness levels of the subtypes, both in terms of their ability to replicate within the host, and transmit to a new host. To date, the proportions of competing-strains has been chosen uniformly, with equal numbers of ferrets infected with mixtures of 0:100, 20:80, 50:50, 80:20 and 100:0 percent. Virus-dynamics models (coupled systems of differential equations) are used to capture the within- and between-host dynamics observed in the data.
In this project, we will investigate models of the viral dynamics, and use these in conjunction with optimal design tools to establish the optimal setup for experiments of this type (with respect to, e.g., proportions of strains, time to introduce the susceptible host, etc.), for strains with different levels of fitness, and for differing experimental aims.
This project will be best suited to someone with strong coding skills, and studying a degree in mathematics or statistics eg MSc (Mathematics and Statistics) in applied mathematics, stochastic processes or statistics