Stochastic approximation to the specific response of the immune system

TL;DR

This paper introduces a stochastic model of immune response dynamics, revealing how finite size fluctuations can significantly influence immune behavior and identifying characteristic fluctuation frequencies.

Contribution

It presents a novel stochastic modeling approach for immune responses, incorporating finite size effects and fluctuation analysis beyond mean field approximations.

Findings

Finite size effects can alter immune response regimes.

Existence of characteristic fluctuation frequency.

Model predictions align with Gillespie simulations.

Abstract

We develop a stochastic model to study the specific response of the immune system. The model is based on the dynamical interaction between Regulatory and Effector CD4+ T cells in the presence of Antigen Presenting Cells inside a lymphatic node. At a mean field level the model predicts the existence of different regimes where active, tolerant, or cyclic immune responses are possible. To study the model beyond mean field and to understand the specific responses of the immune system we use the Linear Noise Approximation and show that fluctuations due to finite size effects may strongly alter the mean field scenario. Moreover, it was found the existence of a certain characteristic frequency for the fluctuations. All the analytical predictions were compared with simulations using the Gillespie's algorithm.