A Physics-Flavored Transformer Network for Parametrizing Contraction Dynamics of Engineered Skeletal Muscle Tissues

2026-08-04

AI Summary

This study addresses the limitations of conventional approaches to functional characterization of engineered skeletal muscle (ESM) tissues, which often rely on oversimplified metrics that neglect critical dynamic features, while mechanistic models remain too complex for scalable application. To bridge this gap, the authors propose a hybrid CNN-Transformer architecture that incorporates stretch-exponential physical priors, thereby embedding biophysical constraints directly into the Transformer for the first time. This enables automatic extraction of high-fidelity dynamic parameters from force–time curves. Leveraging a hybrid training strategy—combining synthetic data pretraining with unsupervised self-alignment on real experimental data—the method achieves efficient and scalable phenotypic analysis under limited-sample conditions. It accurately links idealized models with noisy experimental measurements across multiple cell lines, including a Duchenne muscular dystrophy model, facilitating high-throughput biophysical investigation.

Abstract

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.

Problem

• Engineered Skeletal Muscle Tissues
• Contraction Dynamics
• Kinetic Phenotyping
• Biophysical Modeling
• High-throughput Characterization

Innovation

• Physics-Informed Neural Networks
• Transformer Architecture
• Kinetic Phenotyping
• Hybrid Training Paradigm
• Engineered Skeletal Muscle Tissues