Speaker
Prof.
Ville Mustonen
(Sanger Centre)
Description
Understanding the molecular basis of the adaptive evolution
of a population has relevance for important biological
questions. For example, the problem of identifying genetic
variants which underlie drug resistance, a question of
importance for the treatment of pathogens, and of cancer,
can be understood as a matter of inferring selection. A key
problem in discovering variants under positive selection is
the complexity of the underlying evolutionary dynamics,
which may involve an interplay between several contributing
processes, including mutation, recombination and genetic
drift. Fortunately, technological advances driven by next
generation sequencing are making it possible to
systematically follow across time how the genomic
composition of a population evolves. However, such data
needs new quantitative methods to fulfil its potential. We
here present our ongoing work on how to use time-resolved
sequence data to draw inferences about the evolutionary
dynamics of a population under study. More specifically, we
describe an analysis of a laboratory evolution experiment
where a yeast cross was exposed to a number of cancer drugs
to study the genetic basis how populations respond to such
external stresses. This work is a collaboration project with
Gianni Liti Lab (Institute of Research on Cancer and Ageing
of Nice).
Primary author
Prof.
Ville Mustonen
(Sanger Centre)
