1. Physics of Living Systems
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Optimal evolutionary decision-making to store immune memory

  1. Oskar H Schnaack
  2. Armita Nourmohammad  Is a corresponding author
  1. Max Planck Institute for Dynamics and Self-organization, Germany
  2. University of Washington, United States
Research Article
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Cite this article as: eLife 2021;10:e61346 doi: 10.7554/eLife.61346


The adaptive immune system provides a diverse set of molecules that can mount specific responses against a multitude of pathogens. Memory is a key feature of adaptive immunity, which allows organisms to respond more readily upon re-infections. However, differentiation of memory cells is still one of the least understood cell fate decisions. Here, we introduce a mathematical framework to characterize optimal strategies to store memory to maximize the utility of immune response over an organism's lifetime. We show that memory production should be actively regulated to balance between affinity and cross-reactivity of immune receptors for an effective protection against evolving pathogens. Moreover, we predict that specificity of memory should depend on the organism's lifespan, and shorter-lived organisms with fewer pathogenic encounters should store more cross-reactive memory. Our framework provides a baseline to gauge the efficacy of immune memory in light of an organism's coevolutionary history with pathogens.

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Numerical data generated for all figures and the corresponding code will be provided for publication.

Article and author information

Author details

  1. Oskar H Schnaack

    Statistical Physics of Evolving Systems, Max Planck Institute for Dynamics and Self-organization, Göttingen, Germany
    Competing interests
    No competing interests declared.
  2. Armita Nourmohammad

    Physics, University of Washington, Seattle, United States
    For correspondence
    Competing interests
    Armita Nourmohammad, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6245-3553


Deutsche Forschungsgemeinschaft (SFB1310)

  • Armita Nourmohammad

Max Planck Society (MPRG funding)

  • Armita Nourmohammad

University of Washington (Royalty Research Fund: A153352)

  • Armita Nourmohammad

Max Planck Institute for Dynamics and Self-organization (Open-access funding)

  • Armita Nourmohammad

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Arvind Murugan, University of Chicago, United States

Publication history

  1. Received: July 22, 2020
  2. Accepted: April 23, 2021
  3. Accepted Manuscript published: April 28, 2021 (version 1)


© 2021, Schnaack & Nourmohammad

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


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