Cheetah: A Computational Toolkit for Cybergenetic Control
| Autor: | Thomas E. Gorochowski, Nigel J. Savery, Antonella La Regina, Claire S. Grierson, Lorena Postiglione, Elisa Pedone, Irene de Cesare, Lucia Marucci, Mario di Bernardo, David Haener, Criseida Zamora, Barbara Shannon |
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| Přispěvatelé: | Pedone, E., De Cesare, I., Zamora-Chimal, C. G., Haener, D., Postiglione, L., La Regina, A., Shannon, B., Savery, N. J., Grierson, C. S., Di Bernardo, M., Gorochowski, T. E., Marucci, L. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
Computer science 01 natural sciences Convolutional neural network Protein expression Computer System Synthetic biology Mice 0302 clinical medicine cybergenetic Mammalian cell Lab-On-A-Chip Devices Image Processing Computer-Assisted Segmentation Control (linguistics) 0303 health sciences Microscopy Mouse Embryonic Stem Cells General Medicine Thresholding U-Net Data Accuracy Synthetic Biology Microfluidics Biomedical Engineering Reproducibility of Result Optogenetics Biochemistry Genetics and Molecular Biology (miscellaneous) Cell Line 03 medical and health sciences Computer Systems 010608 biotechnology Escherichia coli Animals Bespoke 030304 developmental biology business.industry Animal Deep learning Reproducibility of Results deep learning Mouse Embryonic Stem Cell Image segmentation Computer architecture Lab-On-A-Chip Device Artificial intelligence business image analysi 030217 neurology & neurosurgery Software |
| Zdroj: | ACS Synthetic Biology |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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