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CellsBox SIGNED

CellsBox: a modular system for automated cell imaging experiments

Total Cost €

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EC-Contrib. €

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Partnership

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Project "CellsBox" data sheet

The following table provides information about the project.

Coordinator
THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE 

Organization address
address: TRINITY LANE THE OLD SCHOOLS
city: CAMBRIDGE
postcode: CB2 1TN
website: www.cam.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 149˙931 €
 EC max contribution 149˙931 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-PoC
 Funding Scheme ERC-POC
 Starting year 2019
 Duration (year-month-day) from 2019-03-01   to  2020-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE UK (CAMBRIDGE) coordinator 149˙931.00

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 Project objective

'Based on research from ERC CoG HydroSync where we developed both hardware and software approaches to cell imaging, we are in the position to develop translationally a cell culture and imaging unit, with integrated modular hardware and analysis software allowing automated robust performance. This promises to not just significantly bring down the costs for a broad spectrum of cell biology and single cell imaging experiments. It will also make these experiments more reproducible, systematic and easily accessible in standardised fashion. Perhaps most important of all, by designing in an integrated (whilst modular architecture) system all the components of the experiment (optics, mechanics, cell environment and fluids control, cell sample chambers, analysis), we make it possible to rapidly feedback information on the state of the sample into actions by any of the other modules, thus allowing a new space of experimental design. This automation in running the experimental stage of cell biology, microbiology, infectious disease models, early embryo developmental work, etc (i.e. any of the many situations where one aims to follow the properties of individual cells) also aligns to the current revolution triggered by machine and deep learning approaches. We can imagine a day when integrated experimental 'CellsBoxes' perform, in a hypothesis-driven optimised and tireless fashion, a battery of experiments that today would simply be inconceivable. This project addresses one of the experimental bottlenecks that still make too much of biological and medical research subject to bias and poor reproducibility, and change the nature of point-of-care cell tissue analysis. It will be disruptive in the current landscape of optical cell imaging, a market > $1bn globally.'

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The information about "CELLSBOX" are provided by the European Opendata Portal: CORDIS opendata.

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lastchecktime (2022-05-19 9:20:23) correctly updated