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Abstract
Natural evolution tackles optimization by producing many genetic variants and exposing these variants to selective pressure, resulting in the survival of the fittest. We use high throughput screening of large libraries of materials with differing surface topographies to probe the interactions of implantable device coatings with cells and tissues. However, the vast size of possible parameter design space precludes a brute force approach to screening all topographical possibilities. Here, we took inspiration from Nature to optimize materials surface topographies using evolutionary algorithms. We show that successive cycles of material design, production, fitness assessment, selection, and mutation results in optimization of biomaterials designs. Starting from a small selection of topographically designed surfaces that upregulate expression of an osteogenic marker, we used genetic crossover and random mutagenesis to generate new generations of topographies.
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1 Eindhoven University of Technology, Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven, The Netherlands (GRID:grid.6852.9) (ISNI:0000 0004 0398 8763)
2 Maastricht University, MERLN Institute for Technology-Inspired Regenerative Medicine, Department of Cell Biology-Inspired Tissue Engineering, Maastricht, The Netherlands (GRID:grid.5012.6) (ISNI:0000 0001 0481 6099)
3 Commonwealth Scientific and Industrial Research Organisation, Materials Science & Engineering, Clayton, Australia (GRID:grid.1016.6) (ISNI:0000 0001 2173 2719); Monash Institute of Pharmaceutical Sciences, Monash Univeristy, Parkville, Australia (GRID:grid.1002.3) (ISNI:0000 0004 1936 7857); Latrobe Institute for Molecular Science, La Trobe University, Melbourne, Australia (GRID:grid.1018.8) (ISNI:0000 0001 2342 0938); University of Nottingham, School of Pharmacy, Nottingham Park, UK (GRID:grid.4563.4) (ISNI:0000 0004 1936 8868)
4 Broad Institute of MIT and Harvard, Imaging Platform, Cambridge, USA (GRID:grid.66859.34)