Predicting the Evolution of Influenza

Much of the burden of infectious disease today is caused by antigenically variable pathogens that can escape from immunity induced by prior infection or vaccination. Influenza viruses are classic examples of such pathogens and also unique in that an extensive worldwide surveillance network exists that routinely tracks both the genetic and antigenic evolution of the virus, analyzing ~20,000 strains each year. Further many aspects of the virus are relatively easy to work with in the laboratory making it an excellent model system for studying such antigenically variable pathogens. Recent advances in my laboratory have resulted in computational an experimental methods that can, to some degree, predict the evolution of the virus. Despite all of this, the governing evolutionary processes governing this evolution are not understood, and experimental work on what we think is the key protein involved in this evolution, at the level of detail that appears necessary, is beyond current experimental techniques. The goal of this PhD project is to study this detail using computational approaches. In particular, to address a fundamental issue in the data that are used as the basis for all of this work. Sam has worked with us as an undergraduate looking at this topic and has made important insights that look highly likely to substantially improve the basic measurements used in understanding and predicting the evolution of influenza viruses. This is a tremendously exciting prospect.

Sam will have real-time access to the genetic and antigenic data on the 20,000 strains of influenza virus collected worldwide each year, and the experimental data generate by our wet-lab collaborations--these data will form the foundation on which to test hypotheses and predictions that this project will generate.