How a well-adapting immune system remembers

TL;DR

This paper models the adaptive immune system as a dynamic Bayesian process that balances new pathogen evidence with past experiences to optimize immune memory and response, explaining observed immune system behaviors.

Contribution

It introduces a Bayesian framework for immune memory updating, linking immune system dynamics to environmental sparsity and vaccine response, providing a novel theoretical perspective.

Findings

Memory pool increases rapidly early in life

Memory repertoires reduce infection costs

Optimal update scheme aligns with competitive receptor dynamics

Abstract

An adaptive agent predicting the future state of an environment must weigh trust in new observations against prior experiences. In this light, we propose a view of the adaptive immune system as a dynamic Bayesian machinery that updates its memory repertoire by balancing evidence from new pathogen encounters against past experience of infection to predict and prepare for future threats. This framework links the observed initial rapid increase of the memory pool early in life followed by a mid-life plateau to the ease of learning salient features of sparse environments. We also derive a modulated memory pool update rule in agreement with current vaccine response experiments. Our results suggest that pathogenic environments are sparse and that memory repertoires significantly decrease infection costs even with moderate sampling. The predicted optimal update scheme maps onto commonly considered competitive dynamics for antigen receptors.