Place a drop of pond water under the microscope, and you will likely find an ocean of extraordinary and diverse single-celled organisms called ciliates. This remarkable group of single-celled organisms wield microtubules, active systems, electrical signaling, and chemical sensors to build intricate geometrical structures and perform complex behaviors that can appear indistinguishable from those of macroscopic animals. Advances in computer vision and machine learning are making it possible to completely digitize and track the dynamics of complex ciliates and mine these data for the hidden structure, patterns, and motifs that are responsible for their behaviors. By deconstructing the diversity of ciliate behaviors in the natural world, themes for organizing and controlling matter at the microscale are beginning to take hold, suggesting new modular approaches for the design of autonomous molecular machines that emulate nature’s finest examples.

在显微镜下滴一滴池塘水，您可能会发现一个充满纤毛的单细胞有机体海洋。这一组非凡的单细胞生物利用微管，活动系统，电信号和化学传感器来构建复杂的几何结构并执行复杂的行为，这些行为与宏观动物的行为无法区分。计算机视觉和机器学习的进步使得可以完全数字化并跟踪复杂纤毛的动态，并挖掘这些数据来寻找对其行为负责的隐藏结构，图案和图案。通过解构自然界纤毛虫行为的多样性，在微观尺度上组织和控制物质的主题开始占主导地位，这为模仿自然界最好例子的自主分子机器的设计提出了新的模块化方法。