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Title: 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 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.  more » « less
Award ID(s):
1734030
NSF-PAR ID:
10099882
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
116
Issue:
18
ISSN:
0027-8424
Page Range / eLocation ID:
8815 to 8823
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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