Yilmaz L Safak, Walhout Albertha Jm
Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, United States.
Curr Opin Chem Biol. 2017 Feb;36:32-39. doi: 10.1016/j.cbpa.2016.12.025. Epub 2017 Jan 12.
Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.
利用基因组规模代谢网络模型(GSMNM)进行通量平衡分析(FBA),能够对多种生物体的代谢进行系统水平的预测。根据所应用的实验约束条件,从交换通量的测量到基因表达数据的整合,可以做出不同精度水平的不同类型预测。使用模式生物进行代谢网络建模开创了该领域的方法发展。此外,模式生物的GSMNMs有助于对代谢进行基本理解,对于动物模型而言,有助于研究人类代谢疾病。在此,我们讨论最常用模式生物的GSMNMs,重点是近期的重建。