Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 116, Issue 18, Pages 8815-8823Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1812810116
Keywords
immune repertoire; Bayesian prediction; biophysics; immune memory; stochastic dynamics
Categories
Funding
- European Research Council [306312]
- Lewis-Sigler fellowship
- Simons Foundation Mathematical Modeling of Living Systems Grant [400425]
- NSF [PHY-1607611, PHY-1734030]
- European Research Council (ERC) [306312] Funding Source: European Research Council (ERC)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available