Evolutionary design of optimal surface topographies for biomaterials

Aliaksei Vasilevich, Aurélie Carlier, David A. Winkler, Shantanu Singh, Jan de Boer

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Article number22160
Number of pages10
JournalScientific Reports
Volume10
Issue number1
DOIs
Publication statusPublished - Dec 2020

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