Kim Hyun Uk, Kim Tae Yong, Lee Sang Yup
Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea.
BMC Syst Biol. 2011;5 Suppl 2(Suppl 2):S14. doi: 10.1186/1752-0509-5-S2-S14. Epub 2011 Dec 14.
Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology.
We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model.
After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.
基因组规模的代谢网络模型有助于阐明生物学现象，并预测用于生物技术应用的基因工程靶点。随着它们的重要性日益增加，其精确的网络表征对于更好地理解细胞生理学也至关重要。
我们在此引入一种用于网络模块化和贝叶斯网络分析的框架（FMB），以研究生物体在扰动下的代谢。FMB揭示了代谢模块之间的影响方向，其中具有相似或正相关通量变化模式的反应会根据代谢通量数据对特定扰动进行聚类。利用在对照和扰动条件下基于约束的通量分析计算得到的代谢通量数据，FMB本质上通过代谢模块水平的网络模块化和贝叶斯网络分析揭示了特定扰动对生物系统的影响。作为一个示例，该框架利用其基因组规模的代谢网络模型应用于基因扰动的大肠杆菌代谢，即lpdA基因敲除突变体。
毕竟，它提供了响应扰动的代谢通量分布的替代情景，这与从传统可用的全基因组高通量技术或代谢通量分析获得的数据互补。
