Cheetah: A Computational Toolkit for Cybergenetic Control

Pedone, Elisa; Cesare, Irene de; Zamora-Chimal, Criseida G.; Haener, David; Postiglione, Lorena; Regina, Antonella La; Shannon, Barbara; Savery, Nigel J.; Grierson, Claire; Bernardo, Mario di; Gorochowski, Thomas E.; Marucci, Lucia

doi:10.1021/acssynbio.0c00463

Cited by 25 publications

(20 citation statements)

References 61 publications

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“…Skip-connections allow the network to retain high-resolution information needed to construct the mask at the end of the network. This approach has been widely successful for segmentation of cells [10][11][12][13][14] and for tracking cells from frame-to-frame within time-lapse images [11,13].…”

Section: Introductionmentioning

confidence: 99%

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

O’Connor

Alnahhas²,

Lugagne³

et al. 2022

PLoS Comput Biol

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Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

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Section: Introductionmentioning

confidence: 99%

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

O’Connor

Alnahhas²,

Lugagne³

et al. 2022

PLoS Comput Biol

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“…Furthermore, advanced computational tools and artificial intelligence (AI) protocols currently in development warrant explosive progress in the field of multimodal assays of cellular platforms. For instance, Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control, has been recently introduced [ 43 ]. Central to the platform is an image segmentation system based on convolutional neural networks supplemented with functionality to count, characterize and control (bacterial and mammalian) cells’ growth and protein expression over time.…”

Section: (Label-free) Analytical Tools Relevant For Optogenetic Cell Platformsmentioning

confidence: 99%

Advanced Optogenetic-Based Biosensing and Related Biomaterials

et al. 2021

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The ability to stimulate mammalian cells with light, brought along by optogenetic control, has significantly broadened our understanding of electrically excitable tissues. Backed by advanced (bio)materials, it has recently paved the way towards novel biosensing concepts supporting bio-analytics applications transversal to the main biomedical stream. The advancements concerning enabling biomaterials and related novel biosensing concepts involving optogenetics are reviewed with particular focus on the use of engineered cells for cell-based sensing platforms and the available toolbox (from mere actuators and reporters to novel multifunctional opto-chemogenetic tools) for optogenetic-enabled real-time cellular diagnostics and biosensor development. The key advantages of these modified cell-based biosensors concern both significantly faster (minutes instead of hours) and higher sensitivity detection of low concentrations of bioactive/toxic analytes (below the threshold concentrations in classical cellular sensors) as well as improved standardization as warranted by unified analytic platforms. These novel multimodal functional electro-optical label-free assays are reviewed among the key elements for optogenetic-based biosensing standardization. This focused review is a potential guide for materials researchers interested in biosensing based on light-responsive biomaterials and related analytic tools.

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“…To assess the noise in the system, we resorted to the commonly-used metrics based on stationary mean and variance to determine cell-to-cell variability [21,42]: (i) the stationary variance or coefficient of variation (CV) of the probability distribution of mRNA or protein copy number per cell, CV = σ/µ, i.e., the ratio of the standard deviation, σ, to the mean, µ, which is a "straightforward estimate for the overall population variability" [21]; (ii) the noise strength or Fano factor (FF), FF = σ 2 /µ, which "reports the fold change in CV 2 with respect to the Poisson process" [8], FF = CV 2 /CV 2 Poisson . That is, it indicates the deviation of regulated gene expression from the Poissonian distribution generally associated with the constitutive gene expression [8,19,22,23].…”

Section: Stochasticity In Promoter Designmentioning

confidence: 99%

“…The applications in this field are designed by channelling the quantitative understanding of the molecular processes to a methodological workflow that can be compared to the use of mechanics in civil engineering. In this respect, very much like computer-aided design became an essential element of mature engineering disciplines, synthetic biology calls for computational methods for containing and accelerating the design process, see, e.g., in [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design

Righetti

Uluşeker

Kahramanoğulları

2021

Biology

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The design and development of synthetic biology applications in a workflow often involve connecting modular components. Whereas computer-aided design tools are picking up in synthetic biology as in other areas of engineering, the methods for verifying the correct functioning of living technologies are still in their infancy. Especially, fine-tuning for the right promoter strength to match the design specifications is often a lengthy and expensive experimental process. In particular, the relationship between signal fidelity and noise in synthetic promoter design can be a key parameter that can affect the healthy functioning of the engineered organism. To this end, based on our previous work on synthetic promoters for the E. coli PhoBR two-component system, we make a case for using chemical reaction network models for computational verification of various promoter designs before a lab implementation. We provide an analysis of this system with extensive stochastic simulations at a single-cell level to assess the signal fidelity and noise relationship. We then show how quasi-steady-state analysis via ordinary differential equations can be used to navigate between models with different levels of detail. We compare stochastic simulations with our full and reduced models by using various metrics for assessing noise. Our analysis suggests that strong promoters with low unbinding rates can act as control tools for filtering out intrinsic noise in the PhoBR context. Our results confirm that even simpler models can be used to determine promoters with specific signal to noise characteristics.

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Cheetah: A Computational Toolkit for Cybergenetic Control

Cited by 25 publications

References 61 publications

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Advanced Optogenetic-Based Biosensing and Related Biomaterials

Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design

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