Advances in microscopy, microfluidics and optogenetics enable single cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah - a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterise and control cells over time. We demonstrate Cheetah's core capabilities by analysing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells.

中文翻译：

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猎豹：用于计算机遗传控制的计算工具包

显微镜技术，微流控技术和光遗传学的进步使单细胞监测和环境调节成为可能，并提供了控制细胞表型的手段。这种系统的开发具有挑战性，通常会导致定制设置，从而阻碍了可重复性。为了解决这个问题，我们引入了Cheetah-一种灵活的计算工具包，可简化实时显微镜分析与细胞控制算法的集成。该平台的中心是基于多功能U-Net卷积神经网络的图像分割系统。补充了功能，可以随着时间的推移对细胞进行可靠的计数，表征和控制。我们通过分析细菌和哺乳动物细胞的长期生长以及通过动态控制哺乳动物细胞中蛋白质的表达来证明猎豹的核心能力。在所有情况下，猎豹的分割精度超过了常用的基于阈值的方法，可以生成更精确的控制信号。这个易于使用的平台的可用性将使控制工程技术更易于访问，并提供探测和操纵活细胞的新方法。