论文标题
使用力学和机器学习对膜曲率产生进行建模
Modeling membrane curvature generation using mechanics and machine learning
论文作者
论文摘要
细胞膜的变形调节运输过程，例如胞吐和内吞作用。从经典上讲，Helfrich Continuum模型用于表征细胞调整膜形状变化的力和机械参数。尽管该经典模型有效地捕获了曲率生成，但使用它近似生物过程的核心挑战之一是从一组合理的值中选择一组机械参数（包括弯曲模量和膜张力）。我们使用Helfrich模型从实际的机械参数的随机采样生成大型合成数据集，并使用此数据集来训练机器学习模型。这些模型产生了有希望的结果，准确地对模型行为进行了分类，并通过机械参数预测膜形状。我们还注意到机器学习中的新兴方法可以利用赫尔夫里奇模型的物理见解来提高性能，并更深入了解细胞如何控制膜形状变化。
The deformation of cellular membranes regulates trafficking processes, such as exocytosis and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces and mechanical parameters that cells tune to accomplish membrane shape changes. While this classical model effectively captures curvature generation, one of the core challenges in using it to approximate a biological process is selecting a set of mechanical parameters (including bending modulus and membrane tension) from a large set of reasonable values. We used the Helfrich model to generate a large synthetic dataset from a random sampling of realistic mechanical parameters and used this dataset to train machine learning models. These models produced promising results, accurately classifying model behavior and predicting membrane shape from mechanical parameters. We also note emerging methods in machine learning that can leverage the physical insight of the Helfrich model to improve performance and draw greater insight into how cells control membrane shape change.
