Systematic applications of metabolomics in metabolic engineering.
Systematic applications of metabolomics in metabolic engineering.
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DOI：
10.3390/metabo2041090
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发表时间：
2012-12-14
期刊：
影响因子：
4.1
通讯作者：
Styczynski MP
中科院分区：
文献类型：
--
作者：
Dromms RA;Styczynski MP
代谢组学提供的生物学信息很好地服务于代谢工程的目标：关于细胞当前如何使用其生化资源的信息可能是告知工程细胞产生目标化合物的策略的最佳方式之一。使用目标化合物（或一些密切相关的分子）的细胞外或细胞内水平的分析来驱动代谢工程是相当常见的。然而，令人惊讶的是，几乎没有系统地使用代谢组学数据集，这些数据集同时测量数百种代谢物，而不仅仅是几种，用于相同的目的。在这里，我们回顾了最常见的系统方法，代谢物数据与代谢工程相结合，重点是现有的努力，使用全代谢组数据集。然后，我们回顾了一些最常见的方法计算建模的细胞范围内的代谢，包括基于约束的模型，并讨论了目前的计算方法，明确使用代谢组学数据。最后，我们讨论了系统地使用代谢组学数据来驱动代谢工程的计算方法的更广泛的潜力。
The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.