Data driven biomedical interface material biology
Jian JI
Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
EXTENDED ABSTRACT: Extracellular matrix (ECM) plays a vital role in defining cell fate in which biochemical signals such as proteins and their derived peptides, mechanical properties such as stiffness and topography can specifically affect cell functions. The combination of the bioinspired multiple molecules and multiscale properties could further enhance the bio-functionality due to the potential additive effect and synergistic enhancement. Recently, high-throughput screening platforms such as droplet microarray and micro-well array have arisen intense attention. High-throughput gradient platforms facilitate screening for plenty of data. Here, we will present our recently work based on rapid buildup microarrays with orthogonal biochemistry gradients via light induced thiol-ene “click” chemistry and label-free identification and counting of live cells by machine learning. The combination of the orthogonal gradients microarray and a rapid cell imaging via machine learning, provide a facial, high-throughput method to study the combinatorial variation of biochemical signals to cell behavior, which prove, for the first time , that the non-specific resistance id more effect to get high EC selective surface for cardiovascular endothelialization.
Prof. Jian Ji is the director of Institute of Biomedical Macromolecule in Zhejiang University, and Qiushi chair professor in Department of Polymer Science and Engineering, Zhejiang University. In 2010, he received the Distinguished Young Scholars Award of the National Science Foundation of China. And in 2015，he was award as Cheung Kong Scholars by Ministry of Education. He is the fellow of The Royal Society of Chemistry and became Associate editor for Journal of Materials Chemistry B since 2018.
His research focuses on the interfacial materiobiology of biomedical implant, tissue engineering and nanomedicine. Several bioinspired methods were explored to develop biocompatible and biofunctional surface for biomedical application. Following the data-driven approach to biomaterials genome initiative, the innovative surface modification methods were combined with high-throughput experimental technology and machine learning, to reveal the quantitative law of synergistic function of different surface properties. The research break through the technical bottleneck of the construction of complicate bioinspired surfaces, and realize the “Bench to Bedside” application in interventional biomedical devices.