Institute of Modern Physics, Chinese Academy of Sciences, 509 Nanchang Rd., Lanzhou, Gansu, 730000, P.R.China.
University of Chinese Academy of Sciences, 19 A Yuquan Rd, Shijingshan District, Beijing, 100049, P.R.China.
Sci Rep. 2017 Jan 3;7:39875. doi: 10.1038/srep39875.
In this study, we analyzed mutants of Clostridium acetobutylicum, an organism used in a broad range of industrial processes related to biofuel production, to facilitate future studies of bioreactor and bioprocess design and scale-up, which are very important research projects for industrial microbiology applications. To accomplish this, we generated 329 mutant strains and applied principal component analysis (PCA) to fermentation data gathered from these strains to identify a core set of independent features for comparison. By doing so, we were able to explain the differences in the mutant strains' fermentation expression states and simplify the analysis and visualization of the multidimensional datasets related to the strains. Our study has produced a high-efficiency PCA application based on a data analytics tool that is designed to visualize screening results and to support several hundred sets of data on fermentation interactions to assist researchers in more precisely screening and capturing high-quality mutant strains. More importantly, although this study focused on the use of PCA in microbial fermentation engineering, its results are broadly applicable.
在这项研究中，我们分析了丙酮丁醇梭菌（Clostridium acetobutylicum）的突变体，这种生物体广泛用于与生物燃料生产相关的各种工业过程，以促进未来对生物反应器和生物工艺设计及放大的研究，这些都是工业微生物学应用的非常重要的研究项目。为了实现这一目标，我们生成了 329 个突变株，并应用主成分分析（PCA）对从这些菌株收集的发酵数据进行分析，以确定用于比较的一组核心独立特征。通过这样做，我们能够解释突变株发酵表达状态的差异，并简化与菌株相关的多维数据集的分析和可视化。我们的研究基于数据分析工具产生了一种高效的 PCA 应用，该工具旨在可视化筛选结果，并支持数百组关于发酵相互作用的数据，以帮助研究人员更精确地筛选和捕获高质量的突变株。更重要的是，尽管本研究侧重于 PCA 在微生物发酵工程中的应用，但其实验结果具有广泛的适用性。
