论文标题
肌球蛋白II丝的基于Python的自动跟踪常规
A Python based automated tracking routine for myosin II filaments
论文作者
论文摘要
物理学家和生物学家对细胞骨架网络中运动蛋白动力学的研究非常感兴趣，以了解单个电动机的动态和特性如何导致合作效应和控制整体网络行为。在这里，我们报告了一种检测和跟踪肌动蛋白网络中肌肉肌球蛋白II丝的方法，该肌动蛋白网络束缚于支持的脂质双层。基于肌球蛋白II丝的特征形状，这种自动跟踪常规使我们能够随着时间的推移遵循肌球蛋白II丝的位置和方向，并根据对位移和时间步长之间的移位分析和角度变化的分析，可靠地将它们的动态分类为扩散和过程中的段。这种自动化的高吞吐量方法将使科学家能够在不同条件下有效地分析运动动力学，并授予比常见跟踪方法提供的更多详细信息的访问，而无需进行及时的手动跟踪或生成Kymographs。
The study of motor protein dynamics within cytoskeletal networks is of high interest to physicists and biologists to understand how the dynamics and properties of individual motors lead to cooperative effects and control of overall network behaviour. Here, we report a method to detect and track muscular myosin II filaments within an actin network tethered to supported lipid bilayers. Based on the characteristic shape of myosin II filaments, this automated tracking routine allowed us to follow the position and orientation of myosin II filaments over time, and to reliably classify their dynamics into segments of diffusive and processive motion based on the analysis of displacements and angular changes between time steps. This automated, high throughput method will allow scientists to efficiently analyse motor dynamics in different conditions, and will grant access to more detailed information than provided by common tracking methods, without any need for time consuming manual tracking or generation of kymographs.
