Huege Jan, Poskar C Hart, Franke Mathias, Junker Björn H
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Physiology and Cell Biology, 06466 Gatersleben, Germany.
Mol Biosyst. 2012 Oct;8(10):2466-9. doi: 10.1039/c2mb25068c.
Research on plant metabolism is currently experiencing the common use of various omics methods creating valuable information on the concentrations of the cell's constituents. However, little is known about in vivo reaction rates, which can be determined by Metabolic Flux Analysis (MFA), a combination of isotope labeling experiments and computer modeling of the metabolic network. Large-scale applications of this method so far have been hampered by tedious procedures of tissue culture, analytics, modeling and simulation. By streamlining the workflow of MFA, the throughput of the method could be significantly increased. We propose strategies for these improvements on various sub-steps which will move flux analysis to the medium-throughput range and closer to established methods such as metabolite profiling. Furthermore, this may enable novel applications of MFA, for example screening plant populations for traits related to the flux phenotype.
目前，植物代谢研究中正在普遍使用各种组学方法，这些方法可生成有关细胞成分浓度的宝贵信息。然而，对于体内反应速率却知之甚少，而代谢通量分析（MFA）可以测定体内反应速率，它是同位素标记实验与代谢网络计算机建模的结合。迄今为止，该方法的大规模应用受到组织培养、分析、建模和模拟等繁琐程序的阻碍。通过简化MFA的工作流程，可以显著提高该方法的通量。我们针对各个子步骤提出了改进策略，这将使通量分析进入中等通量范围，并更接近代谢物谱分析等成熟方法。此外，这可能会使MFA有新的应用，例如筛选具有通量表型相关性状的植物群体。
