人工智能虚拟类器官（AIVOs）

Artificial Intelligence Virtual Organoids (AIVOs)

作者:Long Bai, Jiacan Su

期刊:Bioactive materials

摘要

类器官平台已重塑体外人类生物学研究，但仍受到批次变异、稀缺的纵向读数以及规模化障碍的制约。本文介绍了人工智能虚拟类器官（Artificial Intelligence Virtual Organoids, AIVOs），亦称硅类器官：在计算空间中实现的类器官级数字孪生，虚拟细胞——在必要时亦包括虚拟类器官——作为最小可执行单元。AIVOs融合多模态和纵向测量数据，形成通用状态表征，并利用受生物物理先验约束的虚拟仪器模拟分析和干预，同时结合混合机制模块（基于代理的模型、连续介质模型、有限元模型）以捕捉细胞-细胞、细胞-基质及运输动力学。本文界定了概念边界，形式化了数据-模型-交互架构及构建策略，综述了评估与标准化实践。其应用涵盖药物筛选与剂量设计、疾病亚型分类与抗药性映射、类器官芯片系统集成及临床决策支持。主要挑战包括高质量纵向数据的获取与整合、可扩展计算与模型简化、可解释性与因果推理，以及隐私、安全与公平的治理。虚拟类器官最终将提供一种基于硅的、透明且可复现的桥梁，连接实体类器官与临床实践，实现高通量的计算机模拟实验及主动实验设计，无需额外的实验负担，从而加速精准治疗、机制发现与监管转化。

Abstract

Organoid platforms have reshaped in vitro human biology yet remain constrained by batch variability, sparse longitudinal readouts and barriers to scale. This review introduces Artificial Intelligence Virtual Organoids (AIVOs), also termed silicon organoids: organoid-scale digital twins instantiated in the computational space, with virtual cells-and, where appropriate, virtual organoids-serving as the minimal executable units. AIVOs fuse multimodal and longitudinal measurements into universal state representations and use virtual instruments constrained by biophysical priors to emulate assays and perturbations, while hybrid mechanistic modules (agent-based, continuum, finite-element) capture cell-cell, cell-matrix and transport dynamics. The article defines conceptual boundaries, formalizes a data-model-interaction architecture and construction strategies, and synthesizes evaluation and standardization practices. Applications span drug screening and dosing design, disease subtyping and resistance mapping, integration with organoid-on-chip systems and clinical decision support. Principal challenges include the acquisition and harmonization of high-quality longitudinal data, scalable computation and model reduction, interpretability and causal reasoning, and governance addressing privacy, safety and fairness. Virtual organoids ultimately provide a silicon-grounded, transparent and reproducible bridge between physical organoids and clinical practice, enabling high-throughput in silico experiments and active experiment design without added experimental burden and accelerating precise therapy, mechanism discovery and regulatory translation.

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