Immune digital twins: modeling disease trajectories and therapeutic response

Immune responses are highly dynamic, context-dependent processes shaped by antigen exposure, tissue microenvironment, cross-talk between innate and adaptive immunity, cytokine signaling, metabolism, and therapeutic perturbation. In cancer, chronic infections, autoimmunity, aging, transplantation, and inflammatory disorders, disease progression often arises from dysregulation of immune surveillance or tolerance. Phenomena such as T cell exhaustion, immune evasion, and treatment resistance illustrate how immune dysfunction reflects complex, multi-scale interactions rather than isolated cellular defects. Recent advances in single-cell and spatial omics, immune repertoire profiling, longitudinal clinical data, and wearable monitoring have enabled unprecedented resolution of immune trajectories. Yet, translating these multidimensional datasets into patient-specific predictions remains a formidable challenge. Immune digital twins offer a systems-level framework to integrate multimodal measurements with mechanistic and AI-driven modeling, simulation, and uncertainty-aware inference to construct individualized, dynamically updated representations of immune function.This Research Topic aims to accelerate the conceptual and methodological development of immune digital twins for modeling immune system dynamics, predicting disease trajectories, and optimizing therapeutic interventions. The central challenge is to move beyond static, population-level immune correlates and toward interpretable, dynamically adaptive, and patient-specific computational models that capture immune evolution over time. We welcome contributions addressing how multi-omics data, longitudinal patient profiling, immune-cell state transitions, gene regulatory and signaling networks, and treatment perturbations can be integrated into credible digital twin frameworks. Special emphasis will be placed on hybrid mechanistic–AI approaches, verification and validation, uncertainty quantification, causal and counterfactual inference, benchmarking, model calibration, and translational applications. Ultimately, submissions should demonstrate how immune digital twins can serve as testbeds for hypothesis generation, therapeutic optimization, and precision immunological insights. In all cases, the immune digital twin framework—not the disease context or data modality alone—must constitute the central scientific contribution. This Research Topic welcomes original research, reviews, perspectives, methods, benchmarks, and translational studies wherein the immune digital twin framework forms the primary methodological or conceptual innovation. Studies applying computational or omics analyses without an explicit digital twin component fall outside the scope. To further advance this emerging area, we invite contributions addressing, but not limited to, the following themes:• Multi-omics integration for patient-specific immune modeling• Longitudinal modeling of immune disease trajectories• Digital twins for immunotherapy response prediction and treatment optimization• Hybrid mechanistic and AI/ML models of immune dynamics• Single-cell, spatial, and systems immunology approaches, including gene regulatory and cell–cell communication modeling• Causal inference, counterfactual simulation, and virtual intervention testing• Verification, validation, uncertainty quantification, model calibration, credibility assessment, and benchmarking of immune digital twins• Real-time data assimilation and adaptive updating of patient-specific models• Foundation models and representation learning for immune-state modeling• Translational applications across cancer, autoimmune disease, infectious disease, aging, transplantation, and chronic inflammatory disordersWe particularly encourage submissions that combine methodological rigor with biological interpretability, clinical relevance, and robust validation. Submissions are expected to explicitly position their work within the digital twin paradigm, demonstrating how patient-specific model building, calibration, or validation constitutes a core element of the study.Please note that Dr. Jake Chen is the owner of Medeolinx, LLC and also a scientific advisor to YuvaBio, Inc. Topic editors: Zongliang Yue, Jake Y Chen Source: https://www.frontiersin.org/research-topics/84738/immune-digital-twins-modeling-disease-trajectories-and-therapeutic-response