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.

中文翻译：

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一种基于非参数回归和高斯核算法的新颖方法，可以识别CHO培养基和饲料优化中的关键成分。

由于动物细胞培养基的组成变得越来越复杂，关键变量的识别对于简化和指导随后的培养基优化很重要。然而，传统的实验设计方法是不切实际的，并且在探索如此大的特征空间的能力方面受到限制。因此，在这项工作中，我们开发了NRGK（使用高斯核的非参数回归）方法，该方法旨在确定在细胞培养基开发过程中影响产物效价的关键成分。使用此非参数模型，我们成功地确定了常规PLS（偏最小二乘回归）方法所忽略的重要组成部分。通过ANOVA（方差分析）进一步验证了NRGK方法的优越性。另外，事实证明，由于NRGK方法能够模拟培养基成分和滴定度之间的非线性和线性关系，因此提高了选择精度。此NRGK方法的应用为更精确地识别关键组件提供了新的视角，这些关键组件进一步使得可以在更短的时间内优化媒体。