| AVS 60th International Symposium and Exhibition | |
| Biomaterial Interfaces | Monday Sessions |
| Session BI+AS+IS+NL-MoM |
| Session: | Surfaces to Control Cell Response |
| Presenter: | V.C. Epa, CSIRO Materials Science & Engineering, Australia |
| Authors: | V.C. Epa, CSIRO Materials Science & Engineering, Australia D.A. Winkler, CSIRO Materials Science & Engineering, Australia A.L. Hook, University of Nottingham, UK C. Chang, University of Nottingham, UK J. Yang, University of Nottingham, UK R. Langer, MIT D.G. Anderson, MIT P. Williams, University of Nottingham, UK M.C. Davies, University of Nottingham, UK M.R. Alexander, University of Nottingham, UK |
| Correspondent: | Click to Email |
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, tissues, or pathogens is being increasingly undertaken using high throughput synthesis and assessment methods.
In particular, culture of multipotent cells such as stem cells is a major research focus in regenerative medicine. Much research effort is focused on designing chemically defined, serum-free, feeder-free synthetic substrates and media to support robust self-renewal of pluripotent cells. Changes in cellular properties such as adhesion, morphology, motility, gene expression and differentiation are influenced by surface properties of the materials on which cells have been cultured. Similarly, designing new materials to control the growth of pathogens on implantable and indwelling devices such as pacemakers, and catheters, is critical given the high level of device-centred infections.
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 platform modelling methods by developing the first robust, predictive, quantitative, and purely computational models of adhesion of human embryonic stem cell embryoid bodies, and three clinically important pathogens, Staphylococcus aureus, Pseudomonas aeruginosa, and uropathogenic Escherichia coli, to the surfaces of 496 polymers.