Energy Systems Modeling and Optimization Unit, National Laboratory for Energy and Geology, Lisbon, Portugal.
Biotechnol Bioeng. 2012 Sep;109(9):2279-85. doi: 10.1002/bit.24502. Epub 2012 Apr 11.
Monitoring plasmid production systems is a lab intensive task. This article proposes a methodology based on FTIR spectroscopy and the use of chemometrics for the high-throughput analysis of the plasmid bioproduction process in E. coli. For this study, five batch cultures with different initial medium compositions are designed to represent different biomass and plasmid production behavior, with the maximum plasmid and biomass concentrations varying from 11 to 95 mg L(-1) and 6.8 to 12.8 g L(-1), respectively, and the plasmid production per biomass varying from 0.4 to 5.1 mg g(-1). After a short sample processing consisting of centrifugation and dehydration, the FTIR spectra are recorded from the collected cellular biomass using microtiter plates with 96 wells. After spectral pre-processing, the predictive FTIR spectra models are derived by using partial least squares (PLS) regression with the wavenumber selection performed by a Monte-Carlo strategy. Results show that it is possible to improve the PLS models by selecting specific spectral ranges. For the plasmid model, the spectral regions between 590-1,130, 1,670-2,025, and 2,565-3,280 cm(-1) are found to be highly relevant. Whereas for the biomass, the best wavenumber selections are between 900-1,200, 1,500-1,800, and 2,850-3,200 cm(-1). The optimized PLS models show a high coefficient of determination of 0.91 and 0.89 for the plasmid and biomass concentration, respectively. Additional PLS models for the prediction of the carbon sources glucose and glycerol and the by-product acetic acid, based on metabolism-induced correlations between the nutrients and the cellular biomass are also established.
监测质粒生产系统是一项实验室密集型任务。本文提出了一种基于傅里叶变换红外光谱（FTIR）和化学计量学的方法，用于高通量分析大肠杆菌中质粒生物生产过程。在这项研究中，设计了五个具有不同初始培养基组成的分批培养物，以代表不同的生物量和质粒生产行为，最大质粒和生物量浓度分别从 11 到 95mg/L 和 6.8 到 12.8g/L 变化，质粒生产与生物量的比例从 0.4 到 5.1mg/g 变化。在经过短暂的样品处理（包括离心和脱水）后，使用 96 孔微孔板从收集的细胞生物量中记录 FTIR 光谱。在光谱预处理后，通过使用蒙特卡罗策略选择波数的偏最小二乘（PLS）回归来得出预测 FTIR 光谱模型。结果表明，可以通过选择特定的光谱范围来改进 PLS 模型。对于质粒模型，发现 590-1130、1670-2025 和 2565-3280cm-1 之间的光谱区域具有高度相关性。而对于生物量，最佳的波数选择是在 900-1200、1500-1800 和 2850-3200cm-1 之间。优化的 PLS 模型分别对质粒和生物量浓度的决定系数为 0.91 和 0.89。还建立了基于营养物质与细胞生物量之间代谢诱导相关性的葡萄糖和甘油碳源和副产物乙酸的预测的额外 PLS 模型。
