How a well-adapting immune system remembers

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 en...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS Vol. 116; no. 18; p. 8815
Main Authors: Mayer, Andreas, Balasubramanian, Vijay, Walczak, Aleksandra M, Mora, Thierry
Format: Journal Article
Language:English
Published: United States 30.04.2019
Subjects:
Adaptation, Physiological - immunology
Adaptive Immunity - physiology
Animals
Host-Pathogen Interactions
Immunologic Memory - physiology
Lymphocytes - physiology
Models, Biological
immune repertoire
Bayesian prediction
immune memory
biophysics
stochastic dynamics
ISSN:1091-6490, 1091-6490
Online Access:Get more information
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Summary: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 midlife 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.
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ISSN:1091-6490
1091-6490
DOI:10.1073/pnas.1812810116