"Pathogen Phylodynamics: Unifying Evolution, Infection and Immunity"
Date du début: 1 mai 2014,
Date de fin: 30 avr. 2019
"Genetic sequences represent a rich source of information about evolutionary and ecological processes in natural populations. However the development of methods to recover this information is being outpaced by the explosion in sequence data, especially since the introduction of ‘next-generation’ sequencing. This problem is particularly acute for the inter-disciplinary field of pathogen phylodynamics. The rapid evolution of many pathogens means their ecological and evolutionary dynamics occur on the same timescale and therefore new analytical methods are required to study this joint behaviour. Further, the small genome sizes and medical importance of many viruses mean that hundreds of thousands of homologous sequences are already available, and sample sizes will continue to grow. The main goal of this proposal is to develop and apply multiple new frameworks of evolutionary analysis, to unlock the full potential of current data and to exploit new types of sequence data for which no rigorous analytical methods currently exist. Across four related themes I will use these novel methods to answer major unsolved questions about the evolutionary dynamics of viruses and their hosts: (i) How can we measure adaptation in data sets comprising many thousands of genomes? (ii) Can we reveal the adaptation of viral lineages to the genetic variation in immunity present in host populations? (iii) How can we combine mathematical ecological models with viral genomics to better predict the outcome of chronic HIV and hepatitis C virus infection, or the success of anti-viral drug therapy? (iv) Can we apply methods from ecology and evolution to analyse new data on immune receptor diversity, and use them to better understand the dynamics of leukaemia and viral infection? The suite of analytical methods created during this project will open fresh avenues of research, creating opportunities to exploit the future growth in genetic data on biological diversity across many disciplines."
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