Stochastic effects in autoimmune dynamics

TL;DR

This paper introduces a stochastic model of autoimmune response to viral infections, highlighting how randomness can cause oscillations and variability in disease progression, aligning with experimental observations.

Contribution

It presents a novel stochastic framework for autoimmune dynamics, analyzing oscillations and bi-stability, and linking fluctuations to biological parameters.

Findings

Stochasticity causes sustained oscillations around stable states.

Variability in autoimmune progression can be explained by stochastic fluctuations.

Model provides insights into how biological parameters influence immune response variability.

Abstract

Among various possible causes of autoimmune disease, an important role is played by infections that can result in a breakdown of immune tolerance, primarily through the mechanism of "molecular mimicry". In this paper we propose and analyse a stochastic model of immune response to a viral infection and subsequent autoimmunity, with account for the populations of T cells with different activation thresholds, regulatory T cells, and cytokines. We show analytically and numerically how stochasticity can result in sustained oscillations around deterministically stable steady states, and we also investigate stochastic dynamics in the regime of bi-stability. These results provide a possible explanation for experimentally observed variations in the progression of autoimmune disease. Computations of the variance of stochastic fluctuations provide practically important insights into how the size of these fluctuations depends on various biological parameters, and this also gives a headway for comparison with experimental data on variation in the observed numbers of T cells and organ cells affected by infection.