Kim Joonhoon, Reed Jennifer L
DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, United States; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, United States.
DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, United States; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, United States.
Curr Opin Biotechnol. 2014 Oct;29:34-8. doi: 10.1016/j.copbio.2014.02.009. Epub 2014 Mar 14.
Advances in genome-scale metabolic modeling allow us to investigate and engineer metabolism at a systems level. Metabolic network reconstructions have been made for many organisms and computational approaches have been developed to convert these reconstructions into predictive models. However, due to incomplete knowledge these reconstructions often have missing or extraneous components and interactions, which can be identified by reconciling model predictions with experimental data. Recent studies have provided methods to further improve metabolic model predictions by incorporating transcriptional regulatory interactions and high-throughput omics data to yield context-specific metabolic models. Here we discuss recent approaches for resolving model-data discrepancies and building context-specific metabolic models. Once developed highly accurate metabolic models can be used in a variety of biotechnology applications.
基因组规模代谢建模的进展使我们能够在系统层面研究和改造代谢。许多生物体的代谢网络重建已经完成，并且已经开发出计算方法将这些重建转化为预测模型。然而，由于知识不完整，这些重建通常具有缺失或无关的成分及相互作用，这可以通过将模型预测与实验数据进行比对来识别。最近的研究提供了一些方法，通过纳入转录调控相互作用和高通量组学数据来进一步改进代谢模型预测，从而产生特定环境下的代谢模型。在这里，我们讨论解决模型与数据差异以及构建特定环境下代谢模型的最新方法。一旦开发出高度准确的代谢模型，就可以用于各种生物技术应用。
