Nutrition, Metabolism and Genomics Group, Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.
Human and Animal Physiology, Wageningen University & Research, De Elst 1, 6708 WD, Wageningen, The Netherlands.
BMC Bioinformatics. 2021 Nov 29;22(1):574. doi: 10.1186/s12859-021-04488-8.
Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values.
Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes.
We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.
已经开发出几种计算方法，将转录组学数据与基因组规模的代谢重建相结合，以提高细胞内代谢通量分布推断的准确性。尽管现有的方法将转录丰度用作酶活性的替代物，但每种方法都使用不同的假设和假设。大多数方法都隐含地假设转录水平与通过相应功能的通量之间存在比例关系，尽管这些比例常数在已发表的方法中通常没有明确提及或讨论。E-Flux 就是这样一种方法，在该算法中，通量边界与表达数据相关联，因此与高表达基因相关的反应允许携带更高的通量值。
在这里，我们扩展了 E-Flux 并系统地评估了假设的比例常数对模型预测的影响。我们使用了来自发表的大肠杆菌和酿酒酵母实验的数据，并将算法的预测与测量的细胞外和细胞内通量进行了比较。
我们表明，使用比例常数进行详细建模可以极大地影响分析的结果。这提高了准确性，并允许提取更好的生理信息。
