Immunology for physicists

TLDR

The immune system is a complex, dynamic network of over 10⁷ cellular clones that performs pattern recognition, learning, and memory, analogous to the nervous system. The authors aim to introduce immunology to physicists and highlight how statistical physics and mathematical methods have advanced understanding of immune problems. They review key immunological concepts and illustrate applications of physical and mathematical frameworks to these problems.

Abstract

The immune system is a complex system of cells and molecules that can provide us with a basic defense against pathogenic organisms. Like the nervous system, the immune system performs pattern recognition tasks, learns, and retains a memory of the antigens that it has fought. The immune system contains more than ${10}^{7}$ different clones of cells that communicate via cell-cell contact and the secretion of molecules. Performing complex tasks such as learning and memory involves cooperation among large numbers of components of the immune system and hence there is interest in using methods and concepts from statistical physics. Furthermore, the immune response develops in time and the description of its time evolution is an interesting problem in dynamical systems. In this paper, the authors provide a brief introduction to the biology of the immune system and discuss a number of immunological problems in which the use of physical concepts and mathematical methods has increased our understanding.