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Published April 27, 2021 | Version v1
Preprint Open

Cheetah: A Computational Toolkit for Cybergenetic Control

  • 1. Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW, Bristol, UK - School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, BS8 1TD, Bristol, UK
  • 2. Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW, Bristol, UK
  • 3. Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW, Bristol, UK - BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ, Bristol, UK
  • 4. BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ, Bristol, UK - School of Biochemistry, University of Bristol, Biomedical Sciences Building, University Walk, BS8 1TD, Bristol, UK
  • 5. BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ, Bristol, UK - School of Biological Sciences, University of Bristol, Tyndall Avenue, BS8 1TQ, Bristol, UK
  • 6. Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW, Bristol, UK -BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ, Bristol, UK - Department of EE and ICT, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy
  • 7. Department of Engineering Mathematics, University of Bristol, Ada Lovelace Building, University Walk, BS8 1TW, Bristol, UK - School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, BS8 1TD, Bristol, UK - BrisSynBio, Life Sciences Building, Tyndall Avenue, BS8 1TQ, Bristol, UK

Description

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.
 

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Pedone et al 2021.pdf

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Additional details

Funding

COSY-BIO – Control Engineering of Biological Systems for Reliable Synthetic Biology Applications 766840
European Commission