一种用于工程化骨骼肌组织收缩动力学参数化的物理启发式Transformer网络
A Physics-Flavored Transformer Network for Parametrizing Contraction Dynamics of Engineered Skeletal Muscle Tissues
AI总结：
该研究提出物理启发式神经网络（PFNN），将拉伸指数物理模型集成到CNN-Transformer中，通过混合训练范式实现工程化骨骼肌组织收缩动力学的高保真参数化，为高通量生物物理研究提供可靠工具。
AI中文摘要：
工程化骨骼肌组织（ESMs）已成为生物医学疾病建模和药物筛选的关键结构，但其功能表征常依赖峰值力等简单指标，忽略了关键的动力学信息，部分原因是机制模型捕捉这些动力学时存在较高的数学复杂性，而这种复杂性阻碍了该领域的可扩展应用与广泛推广。本文提出一种物理启发式神经网络（PFNN），用于自动化ESMs的动力学表型分析，其架构将拉伸指数物理模型集成到CNN-Transformer中，可直接从力-时间曲线中提取具有物理意义的参数。为解决标记生物数据稀缺的问题，采用混合训练范式：模型先在合成数据上建立“物理直觉”，再对未标记的真实测量数据进行无监督自对齐。结果表明，该物理启发式方法可在包括杜氏肌营养不良模型在内的多种收缩表型和细胞系上实现高保真参数化，其可扩展、自改进的 pipeline 弥合了理想生物物理与嘈杂体外（in vitro）数据间的差距，为高通量生物物理研究提供了可靠工具。
英文摘要：
Engineered Skeletal Muscle Tissues (ESMs) have become a key structure for biomedical disease modeling and pharmacological screening, yet their functional characterization often relies on simplistic metrics like peak force, discarding critical kinetic information. This is partially due to the high level of mathematical complexity which mechanistic models introduce to capture these dynamics. Hence, exactly the complexity prevents scalable application and widespread adaptation in the field. Here we present a Physics-Flavored Neural Network (PFNN) that automates the kinetic phenotyping of ESMs. Our architecture integrates a stretched-exponential physical model into a CNN-Transformer, enabling the extraction of physically meaningful parameters directly from force-time profiles. To address the scarcity of labeled biological data, we employ a hybrid training paradigm: the model develops a "physical intuition" on synthetic data before undergoing unsupervised self-alignment on unlabeled real-world measurements. Our results demonstrate that this physics-flavored approach achieves high-fidelity parameterization across diverse contractile phenotypes and cell lines, including Duchenne Muscular Dystrophy models. Our scalable, self-improving pipeline bridges the gap between idealized biophysics and noisy \emph{in vitro} data, providing a robust tool for high-throughput biophysical research.
