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FAKIR SIGNED

Focal Adhesion Kinetics In nanosurface Recognition

Total Cost €

0

EC-Contrib. €

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Partnership

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Project "FAKIR" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY OF GLASGOW 

Organization address
address: UNIVERSITY AVENUE
city: GLASGOW
postcode: G12 8QQ
website: www.gla.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 2˙128˙895 €
 EC max contribution 2˙128˙895 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2014-CoG
 Funding Scheme ERC-COG
 Starting year 2015
 Duration (year-month-day) from 2015-08-01   to  2020-07-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF GLASGOW UK (GLASGOW) coordinator 2˙128˙895.00

Map

 Project objective

The provision of advanced functional materials in the area of regenerative medicine and discovery applications depends on many different factors to provide the appropriate targeted function. As adherent cells also read their environment through substrate interactions there is a great interest in developing such substrates in a predictable manner. Their first point of contact is through their focal adhesions and it is also though them that forces are applied allowing the cell to migrate and establish cytoskeletal tension which in turn regulates cell function. The objective of this project is to investigate the cell-substrate interaction at the nanoscale and correlate that to the surface topography for predictable biomaterials. Through the application of state-of-the-art nanofabrication we will fabricate precise surface topographies with length scales comparable to the structural units found in the focal adhesions. The aim is to map and understand the topographical influence in the architectural arrangement of the proteins in the adhesions. Aided by high resolution microscopy we will classify cell types on different nanotopographies. Combining that information with machine learning, we will be able to gain information about cell characteristics from the rule set. That information can also be used in reverse to identify cell types with the previously defined characteristic. This approach is similar to face recognition seen on cameras and mobile phones. The proposed research project will not only provide insight to an area of biomaterials not previously explored, yet aim to provide a blueprint for future design of biomaterials.

 Publications

year authors and title journal last update
List of publications.
2017 Hassan M. Rostam, Paul M. Reynolds, Morgan R. Alexander, Nikolaj Gadegaard, Amir M. Ghaemmaghami
Image based Machine Learning for identification of macrophage subsets
published pages: , ISSN: 2045-2322, DOI: 10.1038/s41598-017-03780-z
Scientific Reports 7/1 2019-09-02
2016 E. Huethorst, M. Hortigon, V. Zamora-Rodriguez, P. M. Reynolds, F. Burton, G. Smith, N. Gadegaard
Enhanced Human-Induced Pluripotent Stem Cell Derived Cardiomyocyte Maturation Using a Dual Microgradient Substrate
published pages: 2231-2239, ISSN: 2373-9878, DOI: 10.1021/acsbiomaterials.6b00426
ACS Biomaterials Science & Engineering 2/12 2019-09-02
2016 John M. Stormonth-Darling, Anwer Saeed, Paul M. Reynolds, Nikolaj Gadegaard
Injection Molding Micro- and Nanostructures in Thermoplastic Elastomers
published pages: 964-971, ISSN: 1438-7492, DOI: 10.1002/mame.201600011
Macromolecular Materials and Engineering 301/8 2019-09-02

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