State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian, China.
State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian, China.
Biophys J. 2023 Jun 20;122(12):2489-2499. doi: 10.1016/j.bpj.2023.05.001. Epub 2023 May 5.
The cell elastic modulus (E) is widely used as the mechanics-based marker to analyze the biological effects of substrates on cells. However, the employment of the Hertz model to extract the apparent E can cause errors due to the disobedience of the small deformation assumption and the infinite half-space assumption, as well as an inability to deduct the deformation of the substrate. So far, no model can effectively solve the errors caused by the above-mentioned aspects simultaneously. In response to this, herein, we propose an active learning model to extract E. The numerical calculation with finite element suggests the good prediction accuracy of the model. The indentation experiments on both hydrogel and cell indicate that the established model can efficiently reduce the error caused by the method of extracting E. The application of this model may facilitate our understanding about the role of E in correlating the stiffness of substrate and the biological behavior of cell.
细胞弹性模量（E）常被用作基于力学的标志物，以分析细胞基底的生物效应。然而，由于小变形假设和无限半空间假设的违背，以及无法扣除基底的变形，赫茨模型（Hertz model）在提取表观 E 时会产生误差。到目前为止，还没有模型能够有效地同时解决上述方面引起的误差。针对这一问题，本文提出了一种主动学习模型来提取 E。有限元数值计算表明该模型具有很好的预测精度。水凝胶和细胞的压痕实验表明，所建立的模型可以有效地降低 E 提取方法引起的误差。该模型的应用可能有助于我们理解 E 在关联基底刚度和细胞生物行为中的作用。
