Speakers
Description
Multiple sclerosis (MS) is a complex, multifactorial disease arising from the interplay between immune dysregulation, environmental triggers, and neurodegenerative processes. Traditional mathematical models based on ordinary and partial differential equations have provided important system-level insights, but often face limitations in capturing stochasticity, heterogeneity, and multi-scale dynamics inherent to MS pathogenesis.
Agent-based modelling offers a complementary framework to address these challenges. In this context, the Universal Immune System Simulator for Multiple Sclerosis (UISS-MS) has been developed as a mechanistic platform to reproduce immune system dynamics underlying disease onset, relapse, and progression. The model integrates cellular interactions, antigen recognition processes, molecular mimicry mechanisms, and environmental triggers, including Epstein–Barr virus (EBV) reactivation, within a unified stochastic simulation environment.
UISS-MS enables the generation of virtual patient cohorts, supporting the in silico exploration of disease trajectories and treatment responses, including immune reconstitution therapies such as cladribine. The framework has demonstrated the ability to reproduce clinically observed relapse patterns and inter-patient variability. Ongoing efforts focus on robustness analysis, credibility assessment, and alignment with emerging regulatory expectations for mechanistic models.
These results highlight the potential of agent-based approaches to bridge theoretical immunology and translational application, supporting the development of digital twins and regulatory-grade in silico trials in multiple sclerosis.
