Towards automated control of embryonic stem cell pluripotency

Khazim, Mahmoud; Postiglione, Lorena; Pedone, Elisa; Rocca, Dan L.; Zahra, Carine; Marucci, Lucia

doi:10.1016/j.ifacol.2019.12.240

Cited by 10 publications

(9 citation statements)

References 36 publications

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“…Cells were cultured on gelatinized tissue culture dishes at 37 °C in a 5% CO 2 humidified incubator in Dulbecco’s modified Eagle medium (DMEM; Sigma; D5796) supplemented with 15% fetal bovine serum (Sigma; F7524), nonessential amino acids (Gibco; 11140035), l -glutamine (Gibco; 25030024), sodium pyruvate (Gibco; 11360039), penicillin–streptomycin (Sigma; P4458), 2-mercaptoethanol (Gibco; 31350010), and 10 ng/mL mLIF (Peprotech; 250-02). Both cell lines were transfected with a plasmid to express a transgene to encode nuclear Histone 2B (H2B) tagged with infrared fluorescent protein (iRFP), as shown in ref . The microfluidic device for mammalian cells was designed and characterized, as shown in ref .…”

Section: Methodsmentioning

confidence: 99%

“…The same core functions are implemented for both cell types, making the code flexible for other chassis and applications. The algorithm is easy to use and shows robust segmentation results in external feedback control experiments with microfluidics/microscopy platforms; − ChipSeg can be easily adapted for open-loop experiments and other cell types/experimental settings.…”

Section: Introductionmentioning

confidence: 99%

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ChipSeg: An Automatic Tool to Segment Bacterial and Mammalian Cells Cultured in Microfluidic Devices

et al. 2021

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Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool that segments bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows us to segment individual cells in high cell density microfluidic devices, to quantify fluorescent protein expression over a time-lapse experiment, and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customized for other experimental settings and research aims.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ChipSeg: An Automatic Tool to Segment Bacterial and Mammalian Cells Cultured in Microfluidic Devices

et al. 2021

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“…It can also be seen that there is a better EI as 1.6878 < 2.75, whilst still using less of each input than the SISO responses (630 < 1068 and 450 < 546). Computational methods have been extensively used in the search for effective cancer treatments, with approaches including optimal control to regulate dynamics of different cell populations, possibly accounting for competition mechanisms [1,2,7,10,16,[32][33][34][35][36], and a feedback action to account for changes in the cancer system, either in-silico or in-vivo, to act in real time [9,11,29,37]. This is, to the best of our knowledge, the first attempt of using feedback control to regulate intracellular dynamics in cancer cells.…”

Section: Drug Holidaysmentioning

confidence: 99%

“…Three main approaches have been proven to be effective for the control of different processes (such as gene expression and cell proliferation), namely: i) open-or closed-loop controllers embedded into cells by means of synthetic gene networks [2][3][4][5][6]; ii) external controllers, where the controlled processes are within cells, while the controller (either at single cell or cell-population level) and the actuation functions are implemented externally via microfluidics-optogenetics/microscopy-flow cytometry platforms and adequate algorithms for online cell output quantification and control [7][8][9][10][11][12][13][14][15][16]; iii) multicellular control, where both the control and actuation functions are embedded into cellular consortia [17][18][19][20][21]. Plenty of examples of embedded controllers have been engineered across different cellular chassis; instead, applications of external and multicellular controllers in mammalian cells are scarce and either just theoretical or limited to proof of concepts.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Cancer Cellular Dynamics: A New Strategy for Therapy Design

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Renson

et al. 2022

Preprint

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Recent advancements in Cybergenetics have lead to the development of new computational/experimental platforms that enable to robustly steer cellular dynamics by applying external feedback control. Such technologies have never been applied to regulate intracellular dynamics of cancer cells. Here, we show in silico that Adaptive Model Predictive Control (MPC) can effectively be used to steer signalling dynamics in Non-Small Cell Lung Cancer (NSCLC) cells to resemble those of wild-type cells, and to support the design of combination therapies. Our optimization-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure to minimize both drug-induced toxicity and the possibility to cause resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells for drug combination therapy design.

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“…Generally, these experiments require the cells to be genetically engineered to transmit their state using fluorescence and respond to specific environmental stimuli in a prescribed way. This combination of computational, physical, and genetic aspects has resulted in this type of approach being termed "external cybergenetic control" and has been successfully applied for regulating gene expression and intracellular signalling in yeast [21][22][23][24][25][26] , bacteria 27 and mammalian cells [28][29][30] . Such external feedback control can also be implemented using optogenetics 2,31 and in combination with flow cytometry for online measurement of the control output 32 .…”

Section: Introductionmentioning

confidence: 99%

Cheetah: a computational toolkit for cybergenetic control

Pedone

Cesare

Zamora

et al. 2020

Preprint

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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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Towards automated control of embryonic stem cell pluripotency

Cited by 10 publications

References 36 publications

ChipSeg: An Automatic Tool to Segment Bacterial and Mammalian Cells Cultured in Microfluidic Devices

ChipSeg: An Automatic Tool to Segment Bacterial and Mammalian Cells Cultured in Microfluidic Devices

Model Predictive Control of Cancer Cellular Dynamics: A New Strategy for Therapy Design

Cheetah: a computational toolkit for cybergenetic control

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