Integrating transcriptional activity in genome-scale models of metabolism
Integrating transcriptional activity in genome-scale models of metabolism
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DOI：
10.1186/s12918-017-0507-0
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发表时间：
2017-12-21
影响因子：
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通讯作者：
Elati, Mohamed
中科院分区：
文献类型：
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作者：
Banos, Daniel Trejo;Trebulle, Pauline;Elati, Mohamed
背景资料：基因组规模的代谢模型为研究细胞内发生的不同反应提供了合理的方法。这些模型与基因调控网络的整合是系统生物学的一个热门话题。迄今为止开发的方法主要集中在解决代谢元素，并使用相当直接的方法来评估基因组表达对代谢phenotype.Results的影响：我们在这里提出了一种方法，将这些代谢模型的基因调控网络的逆向工程。我们将我们的方法应用于高维基因表达数据集，以推断背景基因调控网络。然后，我们比较了所得到的表型模拟与其他相关的methods.Conclusions：我们的方法优于其他方法测试，是更强大的噪音。我们还说明了这种方法的实用程序的研究一个复杂的生物现象，在酵母中的二次移位。
Background: Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype.Results: We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods.Conclusions: Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.