构建可组合的多尺度健康数字孪生系统，实现跨层级协同建模。
OmniBioTwin: A System-of-Twinned-Systems Framework for Health Digital Twins
- 将数字孪生拆分为模块化单元，通过显式交互算子连接。
- 在阿尔茨海默病的GLP-1通路中实现分子、细胞与器官级联合建模。
- 适合需跨尺度整合的医学仿真与个性化诊疗研究者。
健康数字孪生（HDTs）有望实现个体化建模与决策支持，但现有方法结构零散：单一器官或任务的单体模型缺乏跨尺度保真度，而系统级孪生又缺少通用架构。本文提出OmniBioTwin，一种系统级孪生框架（SoTS），将HDT组织为模块化计算实体，通过多层网络架构中的显式交互算子耦合。框架包含七层协同设计，涵盖数据融合、自主孪生建模、跨尺度耦合、时间同步及人机协同决策支持。我们以阿尔茨海默病中胰高血糖素样肽-1（GLP-1）信号通路为例，验证了分子、细胞与器官级孪生在统一系统中的构建与耦合能力。
原文摘要 · Abstract (English)
Health digital twins (HDTs) promise patient-specific modeling and decision support but current approaches remain structurally fragmented: monolithic models that address a single organ or task lack cross-scale fidelity, while system-level twins lack generalizable architectural frameworks. We propose OmniBioTwin, a System-of-Twinned-Systems (SoTS) framework that organizes HDTs as modular computational entities coupled through explicit interaction operators within a multi-layer network architecture. The framework comprises seven coordinated layers - spanning data integration, autonomous twin modeling, cross-scale coupling, temporal synchronization, and human-in-the-loop decision support. We demonstrate OmniBioTwin by instantiating a multiscale twin for glucagon-like peptide-1 (GLP-1) signaling pathways in Alzheimer's disease, illustrating how molecular, cellular, and organ-level twins can be composed and coupled within a unified system.
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