Cheetah: A Computational Toolkit for Cybergenetic Control.
ACS Synth Biol
; 10(5): 979-989, 2021 05 21.
Article
in En
| MEDLINE
| ID: mdl-33904719
ABSTRACT
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, characterize, and control cells over time. We demonstrate Cheetah's core capabilities by analyzing 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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Computer Systems
/
Image Processing, Computer-Assisted
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Escherichia coli
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Mouse Embryonic Stem Cells
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Deep Learning
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Microscopy
Limits:
Animals
Language:
En
Journal:
ACS Synth Biol
Year:
2021
Document type:
Article
Affiliation country: