涵盖11种疾病的人类免疫系统的全面机制多细胞模型
A comprehensive mechanistic multicellular model of the human immune system spanning 11 diseases.
摘要(中文译文)
免疫系统是一个由相互作用的细胞、分子和信号组成的动态复杂网络，对健康至关重要。免疫数字孪生（immune digital twins）——即与生物对应物同步演化的免疫系统虚拟表征——为实现预测性、患者特异性模拟提供了路径，但其实现需要能够跨多种细胞类型和疾病背景进行泛化的机制性框架。我们构建了一个基于逻辑的人类免疫系统机制模型，该模型完全基于从449篇文献中提取的人类实验数据。该模型整合了51种固有免疫细胞类型及亚型（如NK细胞、巨噬细胞）和37种适应性免疫细胞类型及亚型（如Th1/2/17、B细胞），以及37种分泌因子和跨越11种疾病条件的1,450个调控相互作用，疾病条件包括九种病原体、1型糖尿病和肺移植。模型预测针对未用于构建的独立体外（in vitro）、离体（ex vivo）和临床观察进行了验证。在38项验证实验中，与已发表文献的一致率在病原体方面为75%至90%。该模型重现了九种感染的病原体特异性细胞因子特征，捕捉了四种共感染场景（MTB-HIV、MTB-蠕虫、SARS-CoV-2-EBV和SARS-CoV-2-蠕虫）中的协同与拮抗效应，并解析了受CMV、EBV和SARS-CoV-2挑战的肺移植中相互竞争的排斥与耐受信号。这一参考模型将多种免疫背景统一于单一可模拟模型中，并生成了可供实验后续验证的可证伪预测的结构化目录。该模型在Cell Collective上公开可用，并以SBML文件形式提供，代表了向临床整合的、用于患者特异性应用的免疫数字孪生迈出的一步。
Abstract
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 -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.
如何引用
AMA
Robert Moore, Lauren Mayo, Dennis Startsev, Bhanwar Lal Puniya, Kashish Poore, Resa Helikar, et al. A comprehensive mechanistic multicellular model of the human immune system spanning 11 diseases.. Frontiers in immunology. 2026; doi:10.3389/fimmu.2026.1732556.
APA
Robert Moore, Lauren Mayo, Dennis Startsev, Bhanwar Lal Puniya, Kashish Poore, Resa Helikar, et al (2026). A comprehensive mechanistic multicellular model of the human immune system spanning 11 diseases.. Frontiers in immunology. https://doi.org/10.3389/fimmu.2026.1732556
GB/T 7714
Robert Moore, Lauren Mayo, Dennis Startsev, Bhanwar Lal Puniya, Kashish Poore, Resa Helikar, et al. A comprehensive mechanistic multicellular model of the human immune system spanning 11 diseases.[J]. Frontiers in immunology, 2026 doi:10.3389/fimmu.2026.1732556.
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