文献类型：期刊文献

英文题名：A novel method based on nonparametric regression with a Gaussian kernel algorithm identifies the critical components in CHO media and feed optimization

作者：Zou, Mao[1];Zhou, Zi-Wei[2];Fan, Li[1];Zhang, Wei-Jian[1];Zhao, Liang[1];Liu, Xu-Ping[1];Wang, Hai-Bin[3];Tan, Wen-Song[1]

机构：[1]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[2]Shanghai Bioengine Scitech Co Ltd, Shanghai 201203, Peoples R China;[3]Hisun Pharmaceut Hangzhou Co Ltd, Hangzhou 311404, Zhejiang, Peoples R China

年份：2020

卷号：47

期号：1

起止页码：63

外文期刊名：JOURNAL OF INDUSTRIAL MICROBIOLOGY & BIOTECHNOLOGY

收录：;EI(收录号：20242616404219);WOS:【SCI-EXPANDED(收录号:WOS:000497805200001)】；

基金：This work was supported by the Fundamental Research Funds for the Central Universities (No. 22221818014).

语种：英文

外文关键词：Variable selection; Nonparametric regression with Gaussian kernel; Chinese hamster ovary cells; Medium optimization

摘要：As the composition of animal cell culture medium becomes more complex, the identification of key variables is important for simplifying and guiding the subsequent medium optimization. However, the traditional experimental design methods are impractical and limited in their ability to explore such large feature spaces. Therefore, in this work, we developed a NRGK (nonparametric regression with Gaussian kernel) method, which aimed to identify the critical components that affect product titres during the development of cell culture media. With this nonparametric model, we successfully identified the important components that were neglected by the conventional PLS (partial least squares regression) method. The superiority of the NRGK method was further verified by ANOVA (analysis of variance). Additionally, it was proven that the selection accuracy was increased with the NRGK method because of its ability to model both the nonlinear and linear relationships between the medium components and titres. The application of this NRGK method provides new perspectives for the more precise identification of the critical components that further enable the optimization of media in a shorter timeframe.