Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Proc Natl Acad Sci U S A. 2013 Aug 20;110(34):14006-11. doi: 10.1073/pnas.1222569110. Epub 2013 Aug 1.
Stochastic gene expression can lead to phenotypic differences among cells even in isogenic populations growing under macroscopically identical conditions. Here, we apply flux balance analysis in investigating the effects of single-cell proteomics data on the metabolic behavior of an in silico Escherichia coli population. We use the latest metabolic reconstruction integrated with transcriptional regulatory data to model realistic cells growing in a glucose minimal medium under aerobic conditions. The modeled population exhibits a broad distribution of growth rates, and principal component analysis was used to identify well-defined subpopulations that differ in terms of their pathway use. The cells differentiate into slow-growing acetate-secreting cells and fast-growing CO2-secreting cells, and a large population growing at intermediate rates shift from glycolysis to Entner-Doudoroff pathway use. Constraints imposed by integrating regulatory data have a large impact on NADH oxidizing pathway use within the cell. Finally, we find that stochasticity in the expression of only a few genes may be sufficient to capture most of the metabolic variability of the entire population.
随机基因表达即使在宏观条件相同的同基因群体中也会导致细胞表型的差异。在这里，我们应用通量平衡分析来研究单细胞蛋白质组学数据对虚拟大肠杆菌群体代谢行为的影响。我们使用最新的代谢重建与转录调控数据相结合，来模拟在有氧条件下生长于葡萄糖最小培养基中的实际细胞。所模拟的群体表现出广泛的生长速率分布，主成分分析用于识别在途径利用方面存在差异的明确亚群。细胞分化为生长缓慢的乙酸分泌细胞和生长迅速的 CO2 分泌细胞，大量处于中间生长速率的细胞从糖酵解途径转变为 Entner-Doudoroff 途径利用。整合调控数据所施加的约束对细胞内 NADH 氧化途径的利用有很大影响。最后，我们发现仅少数基因表达的随机性可能足以捕捉整个群体的大部分代谢变异性。
