A statistical mechanics approach to autopoietic immune networks

TL;DR

This paper introduces a statistical mechanics framework for modeling immune networks, aiming to bridge theoretical immunology and physics, providing a pedagogical and benchmark model for future research in quantitative immunology.

Contribution

It develops a comprehensive, self-consistent model of adaptive immune response using disordered statistical mechanics, serving as a pedagogical and benchmark tool for the field.

Findings

Model captures key features of adaptive immunity

Provides a pedagogical approach for physicists and immunologists

Establishes a foundation for quantitative immunology applications

Abstract

The aim of this work is to try to bridge over theoretical immunology and disordered statistical mechanics. Our long term hope is to contribute to the development of a quantitative theoretical immunology from which practical applications may stem. In order to make theoretical immunology appealing to the statistical physicist audience we are going to work out a research article which, from one side, may hopefully act as a benchmark for future improvements and developments, from the other side, it is written in a very pedagogical way both from a theoretical physics viewpoint as well as from the theoretical immunology one. Furthermore, we have chosen to test our model describing a wide range of features of the adaptive immune response in only a paper: this has been necessary in order to emphasize the benefit available when using disordered statistical mechanics as a tool for the investigation. However, as a consequence, each section is not at all exhaustive and would deserve deep investigation: for the sake of completeness, we restricted details in the analysis of each feature with the aim of introducing a self-consistent model.