Lecturer: Dr. Yuval Elhanati
Affiliation: École Normale Supérieure, Paris
Abstract:
The adaptive immune system can recognize many
different pathogens by maintaining a large diversity
of cells with different membrane receptors. We
study the complex stochastic processes that
generate and shape this ensemble of immune
receptors developing probabilistic models from
statistical inference of high throughput sequencing
data. Our technique based on transfer matrices
learns the probabilistic properties of the generation
process, and finds it to be amazingly universal
across individuals. We then model also selection
pressures on the generated cells, in terms of the
composition of their receptors, again finding
universality, and reduction in diversity. In general
our methods allows us to characterize and study
the diversity distribution of immune repertoires
using available sample data. This can be
invaluable as a baseline for future study of the
system as well as clinical applications, but might
also expand our knowledge on statistical properties
of interacting ensembles.