TY - JOUR T1 - Cheetah: a computational toolkit for cybergenetic control JF - bioRxiv DO - 10.1101/2020.06.25.171751 SP - 2020.06.25.171751 AU - Elisa Pedone AU - Irene de Cesare AU - Criseida Zamora AU - David Haener AU - Lorena Postiglione AU - Barbara Shannon AU - Nigel Savery AU - Claire S. Grierson AU - Mario di Barnardo AU - Thomas E. Gorochowski AU - Lucia Marucci Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/06/26/2020.06.25.171751.abstract N2 - 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.Competing Interest StatementThe authors have declared no competing interest. ER -