Tunable genetic devices through simultaneous control of transcription and translation

Bartoli, Vittorio; Meaker, Grace A.; Bernardo, Mario di; Gorochowski, Thomas E.

doi:10.1101/711275

Cited by 7 publications

(7 citation statements)

References 65 publications

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“…In the context of external feedback control, the combination of deep learning-based label-free cell classification 49,50 , online training approaches, model-free control strategies (e.g. reinforcement learning-based feedback control 51 ), and the availability of tunable genetic parts 28,39,52,53 could be instrumental in unlocking the potential for control engineering techniques in biology. This will open up new avenues to create reliable and robust, self-adaptive synthetic biological systems 54 , much like how control engineering has revolutionised other fields.…”

Section: Discussionmentioning

confidence: 99%

Cheetah: a computational toolkit for cybergenetic control

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Zamora

et al. 2020

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1Advances in microscopy, microfluidics and optogenetics enable single cell monitoring and 2 environmental regulation and offer the means to control cellular phenotypes. The development 3 of such systems is challenging and often results in bespoke setups that hinder reproducibility. To 4 address this, we introduce Cheetah -a flexible computational toolkit that simplifies the integration 5 of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an 6 image segmentation system based on the versatile U-Net convolutional neural network. This is 7 supplemented with functionality to robustly count, characterise and control cells over time. We 8 demonstrate Cheetah's core capabilities by analysing long-term bacterial and mammalian cell 9 growth and by dynamically controlling protein expression in mammalian cells. In all cases, 10 Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, 11 allowing for more accurate control signals to be generated. Availability of this easy-to-use 12 platform will make control engineering techniques more accessible and offer new ways to probe 13 and manipulate living cells. 14 Introduction 15Modern automated microscopy techniques enable researchers to collect vast amounts of single-16 cell imaging data at high temporal resolutions. This has resulted in time-lapse microscopy 17 becoming the go to method for studying cellular dynamics, enabling the quantification of 18 processes such as stochastic fluctuations during gene expression 1-3 , emerging oscillatory 19 patterns in protein concentrations 4 , lineage selection 5,6 , and many more 7 . 20To make sense of microscopy images, segmentation is performed whereby an image is 21 broken up into regions corresponding to specific features of interest (e.g. cells and the 22 background). Image segmentation allows for the accurate quantification of cellular phenotypes 23 encoded by visual cues (e.g. fluorescence) by ensuring only those pixels corresponding to a cell 24 are considered. A range of segmentation algorithms have been proposed to automatically 25 analyse images of various organisms and tissues 3,8-11 . The most common of these are 26 thresholding 12 and seeded watershed 13 methods, which are available in many scientific image 27 processing toolkits. Commercial software packages also implement this type of functionality, 28 enabling both automated image acquisition and analysis (e.g. NIS-Elements, Nikon). While these 29 proprietary systems are user-friendly requiring no programming skills to be used, they are often 30 difficult to tailor for specific needs and cannot be easily extended to new forms of analysis. 31More recently, deep learning-based approaches to image segmentation have emerged 32 7,14-17 . Compared to the more common thresholding-based approaches 12 , deep learning methods 33 tend to require more significant computational resources when running on traditional computer 34 architectures and often require the time-consuming manual step of generating...

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Section: Discussionmentioning

confidence: 99%

Cheetah: a computational toolkit for cybergenetic control

Pedone

Cesare

Zamora

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Similarly, proteins that are more thermodynamically stable may also be more evolvable 63 . Systems could be buffered against environmental and genetic perturbations through the use of a negative feedback [64][65][66] , tunable genetic parts 67 , stringent multi-level regulation 68 or the application of other control engineering principles 69 .…”

Section: Engineering the Production Of Functionmentioning

confidence: 99%

Towards an engineering theory of evolution

2021

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Biological technologies are fundamentally unlike any other because biology evolves. Bioengineering therefore requires novel design methodologies with evolution at their core. Knowledge about evolution is currently applied to the design of biosystems ad hoc. Unless we have an engineering theory of evolution, we will neither be able to meet evolution’s potential as an engineering tool, nor understand or limit its unintended consequences for our biological designs. Here, we propose the evotype as a helpful concept for engineering the evolutionary potential of biosystems, or other self-adaptive technologies, potentially beyond the realm of biology.

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“…Over the past decade, synthetic biologists have developed more advanced methods to control gene expression. These include engineered regulators based on DNA binding proteins such as zinc fingers 12 , TALENs 13 and CRISPRi 14 , RNA-RNA interactions [15][16][17] , posttranscriptional/translational processes such as RNA and protein degradation 18 , as well as using directed evolution to optimize existing inducible systems 19 . This offers a wealth of options to more strictly regulate gene expression through the coupling of multiple forms of regulation (e.g.…”

Section: This Consists Of a Constitutively Expressed Laci Repressor Tmentioning

confidence: 99%

Harnessing the central dogma for stringent multi-level control of gene expression

Greco

Grierson

Gorochowski

2020

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AbstractStrictly controlled inducible gene expression is crucial when engineering biological systems where even tiny amounts of a protein have a large impact on function or host cell viability. In these cases, leaky protein production must be avoided at all costs. Here, we demonstrate how the central dogma offers a simple way to effectively address this challenge. By simultaneously regulating both transcriptional and translational levels, we show how basal expression of an inducible system can be reduced to virtually undetectable levels, with minimal impact on the maximum induced expression rate achieved. Using this approach, we create several stringent expression systems displaying >1000-fold change in their output after induction. Furthermore, we find that multi-level regulation is able to supress transcriptional noise and create a digital-like switch when transitioning between ‘on’ and ‘off’ states. This work provides foundational knowledge and a genetic toolkit of parts to create multi-level gene expression controllers for those working with toxic genes or requiring precise regulation and propagation of cellular signals. It also demonstrates the value of exploring more complex and diverse regulatory designs for synthetic biology.

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Tunable genetic devices through simultaneous control of transcription and translation

Cited by 7 publications

References 65 publications

Cheetah: a computational toolkit for cybergenetic control

Cheetah: a computational toolkit for cybergenetic control

Towards an engineering theory of evolution

Harnessing the central dogma for stringent multi-level control of gene expression

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