Lee Kwang-Min, Gilmore David F
Arkansas State University, Environmental Sciences Program, State University, AR 72467.
Appl Biochem Biotechnol. 2006 Nov;135(2):101-16. doi: 10.1385/abab:135:2:101.
The statistical design of experiments (DOE) is a collection of predetermined settings of the process variables of interest, which provides an efficient procedure for planning experiments. Experiments on biological processes typically produce long sequences of successive observations on each experimental unit (plant, animal, bioreactor, fermenter, or flask) in response to several treatments (combination of factors). Cell culture and other biotech-related experiments used to be performed by repeated-measures method of experimental design coupled with different levels of several process factors to investigate dynamic biological process. Data collected from this design can be analyzed by several kinds of general linear model (GLM) statistical methods such as multivariate analysis of variance (MANOVA), univariate ANOVA (time split-plot analysis with randomization restriction), and analysis of orthogonal polynomial contrasts of repeated factor (linear coefficient analysis). Last, regression model was introduced to describe responses over time to the different treatments along with model residual analysis. Statistical analysis of biprocess with repeated measurements can help investigate environmental factors and effects affecting physiological and bioprocesses in analyzing and optimizing biotechnology production.
实验的统计设计（DOE）是对感兴趣的过程变量的一组预先设定的设置，它为实验规划提供了一种有效的程序。生物过程实验通常会针对每个实验单元（植物、动物、生物反应器、发酵罐或烧瓶），针对几种处理（因素组合）产生连续观测的长序列数据。细胞培养和其他与生物技术相关的实验过去常采用重复测量的实验设计方法，并结合几个过程因素的不同水平来研究动态生物过程。从这种设计收集的数据可以通过几种一般线性模型（GLM）统计方法进行分析，如多变量方差分析（MANOVA）、单变量方差分析（具有随机化限制的时间分割区组分析）以及重复因素的正交多项式对比分析（线性系数分析）。最后，引入回归模型来描述随时间对不同处理的响应以及模型残差分析。对具有重复测量的双过程进行统计分析有助于在分析和优化生物技术生产时研究影响生理和生物过程的环境因素及效应。
