Sub-cellular mechanics plays a crucial role in a variety of biological functions and dysfunctions. Due to the strong structure-function relationship in cytoskeletal protein networks, light can be shed on their mechanical functionality by investigating their structures. Here, we present a data-driven approach employing a combination of confocal live imaging of fluorescent tagged protein networks, in-silico mechanical experiments and machine learning to investigate this relationship. Our designed image processing and nanoFE mechanical simulation framework resolves the structure and mechanical behaviour of cytoskeletal networks and the developed gradient boosting surrogate models linking network structure to its functionality. In this study, for the first time, we perform mechanical simulations of Filamentous Temperature Sensitive Z (FtsZ) complex protein networks with realistic network geometry depicting its skeletal functionality inside organelles (here, chloroplasts) of the moss Physcomitrella patens. Training on synthetically produced simulation data enables predicting the mechanical characteristics of FtsZ network purely based on its structural features ($R^2⩾0.93$), therefore allowing to extract structural principles enabling specific mechanical traits of FtsZ, such as load bearing and resistance to buckling failure in case of large network deformation.

亚细胞力学在多种生物学功能和功能障碍中起着至关重要的作用。由于细胞骨架蛋白网络中强大的结构-功能关系，因此可以通过研究其结构来揭示其机械功能。在这里，我们提出了一种数据驱动的方法，结合了荧光标记蛋白网络的共聚焦实时成像，计算机内机械实验和机器学习来研究这种关系。我们设计的图像处理和nanoFE机械仿真框架解决了细胞骨架网络的结构和机械行为，并开发了将网络结构与其功能联系起来的梯度增强替代模型。在这项研究中，这是第一次假单胞菌。对合成产生的模拟数据进行训练，可以纯粹基于FtsZ网络的结构特征来预测其机械特性（${\mathrm{[R}}^2⩾0.93$），因此可以提取能够实现FtsZ特定机械特性的结构原理，例如在大的网络变形情况下的承重和抗屈曲破坏性。