中文摘要：

高通量测序技术的快速发展催生了涵盖各层次细胞生命活动的组学数据,如转录组学数据、蛋白质组学数据和互作组学数据等。同时,全基因组代谢网络模型在不断完善和增多。整合组学数据,对生物细胞的代谢网络进行更深入的模拟分析成为目前微生物系统生物学研究的热点。目前整合转录组学数据进行全基因组代谢网络分析的方法主要以流量平衡分析（FBA）为基础,通过辨识不同条件下基因表达的变化,进而优化目标函数以得到相应的流量分布或代谢模型。本文对整合转录组学数据的FBA分析方法进行总结和比较,并详细阐述了不同方法的优缺点,为分析特定问题选择合适的方法提供参考。

英文摘要：

With the advent of high-throughput technologies,the field of systems biology has amassed an abundance of developed metabolic network models and ＂omics ＂ data,such as transcriptomic data,proteomic data and interactomic data. How to integrate omics data into metabolic network for further simulation analysis is becoming a hot spot of the microbial systems biology research. Several published studies have successfully demonstrated that the flux balance analysis（ FBA）,a constraint-based modeling approach,can be used to integrate transcriptomic data into genome-scale metabolic network model reconstructions to generate predictive computational models. In this review,we summarize such FBA-based methods for intergrating expression data into genome-scale metabolic network reconstruction,highlighting the advantages as well as the limitations,and offer the suggestion to select appropriate method to a specific issue.