Tillack Jana, Paczia Nicole, Nöh Katharina, Wiechert Wolfgang, Noack Stephan
Institute of Bio-and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
Metabolites. 2012 Nov 21;2(4):1012-30. doi: 10.3390/metabo2041012.
Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The total measurement error of any analytical protocol is the result of an accumulation of single errors introduced by several processing steps. Here, we present a framework for the quantification of intracellular metabolites, including error propagation during metabolome sample processing. Focusing on one specific protocol, we comprehensively investigate all currently known and accessible factors that ultimately impact the accuracy of intracellular metabolite concentration data. All intermediate steps are modeled, and their uncertainty with respect to the final concentration data is rigorously quantified. Finally, on the basis of a comprehensive metabolome dataset of Corynebacterium glutamicum, an integrated error propagation analysis for all parts of the model is conducted, and the most critical steps for intracellular metabolite quantification are detected.
基于模型的分析已成为现代代谢工程和系统生物学不可或缺的一部分，以便了解复杂且无法直接观察的细胞过程。对于定量分析，不仅实验数据，而且测量误差也起着至关重要的作用。任何分析方案的总测量误差都是由几个处理步骤引入的单个误差累积的结果。在此，我们提出了一个用于细胞内代谢物定量的框架，包括代谢组样本处理过程中的误差传播。聚焦于一个特定方案，我们全面研究了所有目前已知且可获取的最终影响细胞内代谢物浓度数据准确性的因素。对所有中间步骤进行建模，并严格量化它们相对于最终浓度数据的不确定性。最后，基于谷氨酸棒杆菌的综合代谢组数据集，对模型的所有部分进行综合误差传播分析，并检测细胞内代谢物定量的最关键步骤。
