<i>In vivo</i>Feedback Control of an Antithetic Molecular-Titration Motif in<i>Escherichia coli</i>using Microfluidics

Bernardo, Di; Shannon,; Zamora,; Postiglione,; Salzano,; Grierson,; Marucci, Lucia; Savery,

doi:10.1101/2020.02.28.952143

Cited by 13 publications

(25 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, we have taken into account the following realistic constraints on the experimental platform [29] :…”

Section: Agent-based Simulations In Bsimmentioning

confidence: 99%

“…Hence, when such an implementation is adopted, the control input will continue to oscillate between its possible values (Figure 3.b). As is common practice in applications where noise and uncertainties are unavoidable, a dead-zone or a delay can be added in the control loop to avoid high frequency oscillations of the control input that may cause excessive stress to cells and to the actuation system [29]. The details of the proof of convergence for the proposed relay controllers is reported in STAR Methods 7.5.…”

Section: Ratiometric Control Of Cell Populations Can Be Achieved By Using External Feedback Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

Controlling reversible cell differentiation for labor division in microbial consortia

Salzano

Fiore

Bernardo

2021

Preprint

View full text Add to dashboard Cite

We address the problem of regulating and keeping at a desired balance the relative numbers between cells exhibiting a different phenotype within a monostrain microbial consortium. We propose a strategy based on the use of external control inputs, assuming each cell in the community is endowed with a reversible, bistable memory mechanism. Specifically, we provide a general analytical framework to guide the design of external feedback control strategies aimed at balancing the ratio between cells whose memory is stabilized at either one of two equilibria associated to different cell phenotypes. We demonstrate the stability and robustness properties of the control laws proposed and validate them in silico implementing the memory element via a genetic toggle-switch. The proposed control framework may be used to allow long term coexistence of different populations, with both industrial and biotechnological applications. Examples include consortia where each population produces a compound of interest or where one population supports the growth of the other which has the role of producing a desired molecule. As a representative example we consider the realistic agent-based implementation of our control strategy to enable cooperative bioproduction in microbial consortia.

show abstract

“…Moreover, we have taken into account the following realistic constraints on the experimental platform [29] :…”

Section: Agent-based Simulations In Bsimmentioning

confidence: 99%

Section: Ratiometric Control Of Cell Populations Can Be Achieved By Using External Feedback Strategiesmentioning

confidence: 99%

Controlling reversible cell differentiation for labor division in microbial consortia

Salzano

Fiore

Bernardo

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Once trained, Cheetah segmentation masks were generated and used to calculate the number of cells and average GFP fluorescence per cell (Figures 2B, 2C). These results were compared to similar analyses using segmentation masks generated by ChipSeg that we 33,41 and others 21,22 have previously implemented in a similar experimental setup (Supplementary Movie 1; Methods). There were several clear differences between the two segmentation methods.…”

Section: Robust Image Segmentation and Analysis Of Bacteria And Mammalian Cellsmentioning

confidence: 99%

“…Results from this experiment and related replica showed that the platform was able to accurately control average mCherry fluorescence in the cells for the duration of the experiment (Figures 3C, 3D; Supplementary Figure 1; Supplementary Movie 3). To evaluate the performance of the control experiment we measured the Integral Square Error (ISE) 41 for both controlled and uncontrolled chambers (i.e. those that received the same input in open loop).…”

Section: External Feedback Control Of Protein Expression In Mammalian Cellsmentioning

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

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...

show abstract

“…The same core functions are implemented for both cell types, making the code flexible for other chassis and applications. The algorithm showed robust segmentation results in external feedback control experiments with microfluidics/microscopy platforms [6][7][8][9] , and can be easily adapted for open-loop experiments, other cell types and experimental settings.…”

Section: Introductionmentioning

confidence: 99%

ChipSeg: an automatic tool to segment bacteria and mammalian cells cultured in microfluidic devices

Cesare

Zamora-Chimal

Postiglione

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. While such image segmentation applications have been previously reported, there is in the literature a lack of open-source and documented code for the community. We describe ChipSeg, a computational tool to segment bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy. The method is based on thresholding and uses the same core functions for both cell types. It allows to segment individual cells in high cell-density microfluidic devices, to quantify fluorescence protein expression over a time-lapse experiment and to track individual cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customised for other experimental settings and research aims.

show abstract

In vivoFeedback Control of an Antithetic Molecular-Titration Motif inEscherichia coliusing Microfluidics

Cited by 13 publications

References 26 publications

Controlling reversible cell differentiation for labor division in microbial consortia

Controlling reversible cell differentiation for labor division in microbial consortia

Cheetah: a computational toolkit for cybergenetic control

ChipSeg: an automatic tool to segment bacteria and mammalian cells cultured in microfluidic devices

Contact Info

Product

Resources

About