Computational Modeling of Immune Response Networks

Abstract

The immune system operates through complex networks of cellular interactions, cytokine signalling cascades, and antigen recognition events that collectively determine responses to pathogens, tumours, and self-antigens, and computational models that simulate these networks are essential for predicting immune behaviour, designing immunotherapies, and understanding autoimmune dysregulation, yet adoption varies across modelling approaches. We evaluated 216 computational immune modelling programmes across centres affiliated with Western Europe Data Science University in Madrid between 2018 and 2022, spanning five model categories: ordinary differential equation immune kinetics models, agent-based immune cell simulations, single-cell immune network inference, immunotherapy response prediction models, and vaccine efficacy simulation frameworks. A Computational Immune Modelling Index (CIMI) was constructed from five sub-scores -- predictive accuracy for immune dynamics, patient-specific parameterisation fidelity, multi-scale integration from molecular to tissue, experimental validation concordance, and therapeutic decision support utility -- with weights from regression against sustained adoption. CIMI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted models with an AUC of 0.882. Immunotherapy response prediction scored highest (mean CIMI 0.824), while agent-based simulations trailed at 0.598. Only 35.6 percent exceeded the 0.75 threshold. Predictive accuracy carried the largest weight (beta = +0.278), followed by therapeutic utility (beta = +0.230).