OmniBioTwin: A System-of-Twinned-Systems Framework for Health Digital Twins
Abstract
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.
Versions
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BibTeX
@misc{wang2026omnibiotwin,
title = {{OmniBioTwin: A System-of-Twinned-Systems Framework for Health Digital Twins}},
author = {Zhaohui Wang and Yu Huang and Jiang Bian},
howpublished = {arXiv},
year = {2026},
month = jun,
doi = {10.48550/arxiv.2606.11264},
eprint = {2606.11264},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.11264},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - OmniBioTwin: A System-of-Twinned-Systems Framework for Health Digital Twins
AU - Zhaohui Wang
AU - Yu Huang
AU - Jiang Bian
PY - 2026
DA - 2026/06/09
PB - arXiv
DO - 10.48550/arxiv.2606.11264
UR - https://arxiv.org/abs/2606.11264
AB - 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.
ER -