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The immune system is a dynamic, complex network of interacting cells, molecules, and signals central to health. Immune digital twins - virtual representations of the immune system that evolve in tandem with their biological counterparts - offer a path toward predictive, patient-specific simulations, but their realization requires mechanistic frameworks that generalize across multiple cell types and disease contexts.

We constructed a logic-based mechanistic model of the human immune system built exclusively on human experimental data extracted from 449 publications. The model integrates 51 innate (e.g., NK cells, macrophages) and 37 adaptive (e.g., Th1/2/17, B cells) immune cell types and subtypes, together with 37 secretory factors and 1,450 regulatory interactions across 11 disease conditions, including nine pathogens, type 1 diabetes, and lung transplantation. Model predictions were validated against independent in vitro, ex vivo, and clinical observations not used during construction.

Across 38 validation experiments, agreement with published literature ranged from 75% to 90% across pathogens. The model reproduced pathogen-specific cytokine signatures for nine infections, captured synergistic and antagonistic effects in four coinfection scenarios (MTB-HIV, MTB-Helminth, SARS-CoV-2-EBV, and Plasmodium falciparum-Helminth), and resolved competing rejection and tolerance signals in lung transplantation challenged by CMV, EBV, and SARS-CoV-2.

This reference model unifies multiple immune contexts within a single simulatable model and generates a structured catalog of falsifiable predictions for experimental follow-up. The model is openly available on Cell Collective and as an SBML file, representing a step toward a clinically integrated immune digital twin for patient-specific applications.