Bridging the Gap - from Simple Competition Models to the Statistical Analysis of Immune Repertoires

Abstract

Simple ecological models of T-cell expansion have had some success in capturing key quantitative features of experimentally observed T-cell dynamics. We considered a model in which T-cell clone expansion is determined by competition for binding with time-dependent antigen levels. We developed numerical methods for simulating T-cell dynamics under such a model as well as statistical methods to bridge the gap between these ecological models and longitudinal repertoire sequencing data. We were able to characterize immune repertoire sequencing noise, which allows us to make testable predictions about how underlying T-cell distributions and dynamics would manifest in repertoire sequencing data.