Machine learning-based prediction for single-cell mechanics

Abstract

Single-cell mechanics have gained much attention due to its importance in a broad range of biological applications. Different experimental approaches have been used for measuring the mechanical properties of individual cells. However, the technical demands and time-consuming nature of these procedures have limited the throughput of single-cell measurement, necessitating the development of alternative computational approaches. Recently, single-cell deformability can be predicted using a convolutional neural network (CNN) model, shedding a light on using machine learning (ML) algorithms for high-throughput characterizations of single-cell mechanical properties. In this work, we developed a novel ML-based computational framework that can reproduce a physical microfluidic system to investigate the individual cell's deformability. The datasets for the training and testing of our model were generated using high-fidelity fluid-structure interaction (FSI) simulations. Our FSI-based ML approach of adopting CNN algorithms demonstrated a highly accurate prediction for the membrane stiffness of a microcapsule (maximum R2 = 0.98) based on its deformed shape. In this paper, we show that by applying physical constraints including the microcapsule's total surface area and total volume, we were able to build a physics-constrained ML model that possesses better convergence and higher stability during both training and validation. Finally, we found that ML models that used the three-dimensional geometry of the capsule as input could outperform the typical CNN models that relied solely on the two-dimensional images. We expect that this physics-constrained computational framework will serve as a basis for developing future tools for real-time biological applications through the integration of high-fidelity simulations with ML algorithms.