How a well-adapting immune system remembers

Area of Science:

• Immunology
• Computational Biology
• Bayesian Inference

Background:

• Adaptive agents must balance new data with prior experience.
• The immune system's memory repertoire is crucial for predicting and preparing for future threats.

Purpose of the Study:

• To model the adaptive immune system as a dynamic Bayesian machinery.
• To link immune memory development to environmental sparsity and infection costs.

Main Methods:

• Developed a Bayesian framework for immune memory repertoire updates.
• Derived a modulated memory pool update rule.
• Analyzed the impact of environmental sparsity on immune memory.

Main Results:

• The model explains the observed pattern of immune memory pool expansion and plateau.
• The derived update rule aligns with experimental vaccine-response data.
• Pathogenic environments are suggested to be sparse, with memory repertoires reducing infection costs.

Conclusions:

• The adaptive immune system functions as a Bayesian system for memory updates.
• Immune memory significantly mitigates infection costs in sparse pathogenic environments.
• The optimal memory update scheme reflects competitive dynamics of antigen receptors.