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PLS-based process analysis for glycosylation reaction

机译：基于PLS的糖基化反应过程分析

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In this work, a novel methodology of evaluating the glycan distribution has been proposed. A multivariate statistical regression method of partial least squares (PLS) is used to establish the relationship between the manipulated variables and the response variables. All the fitting relations are contained in the glycosylation process gain matrix K obtained by PLS. According to the singular value decomposition of K, the importance degree of input enzymes to the desired glycan state is clearly illustrated. After that, we analyze the controllability of the glycan distribution affected by appropriate variables of enzymes and nucleotide sugar donor concentrations. Compared with the results of the method of standard analysis of variance (ANOVA), similar conclusions can be achieved: We can change appropriate manipulated variable concentrations to direct the glycan distribution. For some obvious different results produced by the two methods, we make a detailed analysis and interpretation. First, we will discuss how to implement the design of experiments (DOE), which is based on the glycosylation reaction network model, to generate the output data of glycosylation process; Then the PLS model is established utilizing the glycosylation data to obtain the gain matrix K, and to get the singular value decomposition of the K; Finally, we analyze the controllability of desired glycan state utilizing the singular values σi of the high-dimensional matrix K. The results may provide a foundation for the controlling glycosylation on-line in the future.
机译：在这项工作中，提出了一种评估聚糖分布的新颖方法。使用偏最小二乘（PLS）的多元统计回归方法来建立操纵变量和响应变量之间的关系。所有的拟合关系都包含在通过PLS获得的糖基化过程增益矩阵K中。根据K的奇异值分解，可以清楚地说明输入酶对所需聚糖状态的重要程度。之后，我们分析了受酶和核苷酸糖供体浓度的适当变量影响的聚糖分布的可控性。与方差标准分析（ANOVA）方法的结果相比，可以得出类似的结论：我们可以更改适当的操纵变量浓度，以控制聚糖的分布。对于两种方法产生的一些明显不同的结果，我们进行了详细的分析和解释。首先，我们将讨论如何实施基于糖基化反应网络模型的实验设计（DOE），以生成糖基化过程的输出数据。然后利用糖基化数据建立PLS模型，得到增益矩阵K，并对K进行奇异值分解。最后，我们利用高维矩阵K的奇异值σi分析所需糖基态的可控性。该结果可为将来在线控制糖基化提供基础。

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