Abstract: Several recent studies suggest that negative frequency-dependent selection (NFDS) acting through accessory genome loci is a strong force influencing the population evolution of major human bacterial pathogens. Such models can be fitted using likelihood- free inference for simulator-based statistical models, for example using the ELFI software (elfi.ai). Here we review some of these results and combine them with machine learning to introduce a practical method for optimizing vaccines by jointly using data from genomic population surveys and epidemiological studies. The results may lead to largely improved workflows to design new vaccines.

Bio: Jukka Corander is a professor at the Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Norway, also a professor at the Department of Mathematics and Statistics, University of Helsinki, Finland, and an honorary faculty member at the Wellcome Sanger Institute, Cambridge, UK. He has won two ERC grants, an ERC StG (2009-2014) from the mathematics and statistics panel and an ERC AdG (2017-2022) from the infection and immunity panel. He has published nearly 250 scientific peer-reviewed articles and his main research interests are microbial evolution and transmission modeling, statistical machine learning, population genomics, and inference algorithms. The statistical methods introduced by his research group have led to numerous discoveries on the evolution, resistance, virulence, and transmission of pathogenic bacteria and viruses.

Speaker: Jukka Corander

Affiliation: Professor of Statistics Department of Mathematics and Statistics, University of Helsinki

Place of Seminar: Lecture Hall T5, Konemiehentie 2, Aalto University