Epa V Chandana, Yang Jing, Mei Ying, Hook Andrew L, Langer Robert, Anderson Daniel G, Davies Martyn C, Alexander Morgan R, Winkler David A
CSIRO Materials Science & Engineering, Parkville, Australia.
J Mater Chem. 2012 Sep 18;22(39):20902-20906. doi: 10.1039/C2JM34782B.
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.
设计用于控制生物学过程的材料是生物材料和再生医学研究的重点。利用高通量合成和评估方法，越来越多地开展了对具有适当生物相容性或能够主动控制细胞和组织的材料的发现与设计。我们报告了一种相对简单但强大的机器学习方法，该方法可生成将聚合物或其他材料的微观或分子特性与其生物学效应联系起来的模型。我们通过开发首个关于人类胚胎干细胞胚状体（hEB）与一个包含496种聚合物的微阵列库表面黏附的强大、预测性、定量且完全基于计算的模型，展示了这些方法的潜力。
