American Journal for Young Scientists ยท ISSN 3068-9015
Modeling Relapse and Remission in Multiple Sclerosis Through T Cell Dynamics
Authors: Mahita Chava, Vedant Desikamani
Published: July 22, 2025
Category: Computational Neuroscience
Abstract
Multiple sclerosis is a chronic autoimmune disorder characterized by unpredictable transitions between relapse, remission, and neurological decline. A key driver of this variability lies in the complex interplay between autoreactive T cells that mediate tissue damage and regulatory T cells that suppress autoimmune responses. In this study, we present a minimal yet powerful dynamical systems model that captures the spectrum of immune behaviors observed in MS, including stable remission, periodic flare-ups, and persistent inflammation. Our model couples nonlinear growth, logistic saturation, mutual inhibition, and stochastic perturbations to simulate autoreactive-regulatory T cell dynamics. Phase plane and bifurcation analyses reveal four distinct immune states: (i) immune tolerance (remission), (ii) oscillatory dynamics (cyclical relapses), (iii) excitable regimes (sporadic flares), and (iv) chronic inflammatory states (progressive MS). Time-series simulations replicate clinical trajectories, while parameter sweeps identify thresholds for immune stability. Notably, the model predicts how modest increases in regulatory T-cell efficacy or reductions in pro- inflammatory sensitivity can shift the system from relapse-prone to stable remission. These findings suggest that MS progression may be governed by low-dimensional immune feedback loops, and that precision interventions can be designed by tuning key regulatory parameters. This framework offers a tractable, mechanistic approach for understanding MS heterogeneity and guiding personalized therapeutic strategies.