Duarte Natalie C, Palsson Bernhard Ø, Fu Pengcheng
Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA.
BMC Genomics. 2004 Sep 8;5:63. doi: 10.1186/1471-2164-5-63.
The yeast Saccharomyces cerevisiae is an important microorganism for both industrial processes and scientific research. Consequently, there have been extensive efforts to characterize its cellular processes. In order to fully understand the relationship between yeast's genome and its physiology, the stockpiles of diverse biological data sets that describe its cellular components and phenotypic behavior must be integrated at the genome-scale. Genome-scale metabolic networks have been reconstructed for several microorganisms, including S. cerevisiae, and the properties of these networks have been successfully analyzed using a variety of constraint-based methods. Phenotypic phase plane analysis is a constraint-based method which provides a global view of how optimal growth rates are affected by changes in two environmental variables such as a carbon and an oxygen uptake rate. Some applications of phenotypic phase plane analysis include the study of optimal growth rates and of network capacity and function.
In this study, the Saccharomyces cerevisiae genome-scale metabolic network was used to formulate a phenotypic phase plane that displays the maximum allowable growth rate and distinct patterns of metabolic pathway utilization for all combinations of glucose and oxygen uptake rates. In silico predictions of growth rate and secretion rates and in vivo data for three separate growth conditions (aerobic glucose-limited, oxidative-fermentative, and microaerobic) were concordant.
Taken together, this study examines the function and capacity of yeast's metabolic machinery and shows that the phenotypic phase plane can be used to accurately predict metabolic phenotypes and to interpret experimental data in the context of a genome-scale model.
酿酒酵母是工业生产和科学研究中的一种重要微生物。因此,人们为表征其细胞过程付出了大量努力。为了全面理解酵母基因组与其生理学之间的关系,必须在基因组规模上整合描述其细胞成分和表型行为的各种生物数据集。已经为包括酿酒酵母在内的几种微生物重建了基因组规模的代谢网络,并使用各种基于约束的方法成功分析了这些网络的特性。表型相平面分析是一种基于约束的方法,它提供了一个全局视角,展示了最佳生长速率如何受到两个环境变量(如碳和氧摄取率)变化的影响。表型相平面分析的一些应用包括对最佳生长速率以及网络容量和功能的研究。
在本研究中,酿酒酵母基因组规模的代谢网络被用于构建一个表型相平面,该平面展示了葡萄糖和氧摄取率的所有组合下的最大允许生长速率以及代谢途径利用的不同模式。生长速率和分泌速率的计算机模拟预测与三种不同生长条件(好氧葡萄糖受限、氧化发酵和微需氧)下的体内数据一致。
综上所述,本研究考察了酵母代谢机制的功能和容量,并表明表型相平面可用于准确预测代谢表型,并在基因组规模模型的背景下解释实验数据。