Multi-locus models of pathogen evolution. (360G-Wellcome-101239_Z_13_Z)

Time-resolved genetic data offers a new and exciting opportunity to study pathogen evolution. Sequencing a population at multiple time points reveals genetic changes as they occur. Mathematical models based upon the dynamics of evolutionary systems allow for more accurate identification of alleles under selection, and better measurements of the magnitude of selection, than have previously been achieved. I will develop models to interpret time-resolved genetic data, so as to better understand the evolution of pathogens. Unified by the theme of modelling rapid evolutionary dynamics, this work will make progress in understanding multiple pathogenic organisms. Specifically, this project will use high-coverage sequence data to quantify the role of selection in the intra-patient evolution of influenza, relevant to the emergence of new pandemics. It will examine how immune and drug pressure, acting upon the HIV virus, affect viral diversity in the early stages of an infection. The project will develop methods to better interpret genetic data from drug resistance experiments, in order to identify genomic factors leading to drug resistance in malaria parasite, helminthes, and leishmania. Finally, I will investigate the potential of multi-locus genetic models of evolution to understand, and to predict, the evolution of seasonal influenza.