How a Well-adapting Immune System Remembers

The adaptive immune system uses its past experience of pathogens to prepare for future infections. How much can the adaptive immune system learn about the statistics of changing pathogenic environments given its sampling of the antigenic universe? And how should it best adapt its repertoire of lymphocyte receptor specificities based on its experience? Here, to answer these questions we propose a view of adaptive immunity as a dynamic Bayesian machinery that predicts optimal repertoires based on past pathogen encounters and knowledge about typical pathogen dynamics. Two key experimentally observed characteristics of adaptive immunity emerge naturally from this model: (1) a negative correlation between fold change of protection upon a challenge and preexisting immune levels and (2) differential regulation of memory and naive cells. We argue that to explain the benefits of immune memory, antigenic environments need to be highly sparse. We derive experimentally testable predictions about the diversity of the memory repertoire over time in such sparse antigenic environments. The Bayesian perspective on immunological memory provides a unifying conceptual framework for a number of features of adaptive immunity and suggests further experiments