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中文摘要：
高通量数据的产出为基因组尺度代谢网络的构建提供了基础，但同时也对网络构建和分析方法的改进提出了挑战。随着数据量的不断增大，耗时耗力的人工构建及分析已经无法满足模型发展的需要，因而各种自动化的方法应运而生。模型构建和分析的自动化不仅能够大幅度提高模型构建和解析的速度，同时对于模型构建和分析方法的标准化和程序化也有着不可替代的作用。文中结合作者的实际研究经验，对基因组尺度代谢网络构建的自动化进程和主要的代谢网络分析工具进行了较为详细的介绍，总结了代谢网络自动重构的流程，并提出了目前面对的主要问题和未来的研究方向。
英文摘要：
High-throughput data supply a basis for the reconstruction of genome-scale metabolic networks, andmeanwhile bring challenges to the reconstruction and analysis methods. With the increasing ot oata quanuty, me time-consuming manual reconstruction and analysis are far behind the improvement of models. Therefore, various automatic methods emerge. The automatic reconstruction and analysis have irreplaceable effect in the standardization and programming of reconstruction and analysis methods, as well as largely improving the speed of reconstruction and understanding of the metabolic network. In this review, we introduced the progress of automatic reconstruction and the main analysis tools of genome-scale metabolic network. We further summarized the workflow of automatic reconstruction. The difficulties and perspectives on this research field are also discussed.
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