Modelling human embryoid body cell adhesion to a combinatorial library of polymer surfaces
Open accessAbstract
Designing materials to control biology is an intense focus of biomaterials and regenerative medicine research. Discovering and designing materials with appropriate biological compatibility or active control of cells and tissues is being increasingly undertaken using high throughput synthesis and assessment methods. We report a relatively simple but powerful machine-learning method of generating models that link microscopic or molecular properties of polymers or other materials to their biological effects. We illustrate the potential of these methods by developing the first robust, predictive, quantitative, and purely computational models of adhesion of human embryonic stem cell embryoid bodies (hEB) to the surfaces of a 496-member polymer micro array library.
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- Authors
- Epa, V. C., Yang, J., Mei, Y., Hook, A. L., Langer, R., Anderson, D. G., Davies, M. C., Alexander, M. R., & Winkler, D. A.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- University of Nottingham (GB)
- Koch Institute for Integrative Cancer Research At MIT (US)
- Corresponding author
- Winkler, David A.
- ORCID
- Venue
- Journal of Materials Chemistry
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2012
- Volume
- 22
- Issue
- 39
- Pages
- 20902
- ISSN
- 0959-9428 (ISSN-L)
- 1364-5501
- PMC
- PMC3787298
- OpenAlex
- W2097122306
- Cited by
- 55
- References
- 22
- Citations by year
- 2026: 5 · 2025: 1 · 2024: 4 · 2023: 3 · 2022: 2 · 2021: 6 · 2020: 4 · 2019: 4 · 2018: 3 · 2017: 3 · 2016: 3 · 2015: 6 · 2014: 7 · 2013: 2 · 2012: 1
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of Materials Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Nottingham ePrints (University of Nottingham) (submittedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- PMC
- Funders
- Wellcome Trust (085245)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Surfaces, Coatings and Films › Polymer Surface Interaction Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular Mechanics and Interactions
- Keywords
- cell adhesion, polymer microarrays, machine learning, polymer surface properties, biomaterials, high-throughput materials screening
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