A network-based method for predicting gene-nutrient interactions and its application to yeast amino-acid metabolism
A network-based method for predicting gene-nutrient interactions and its application to yeast amino-acid metabolism
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DOI：
10.1039/b823287n
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发表时间：
2009-01-01
影响因子：
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通讯作者：
Shlomi, Tomer
中科院分区：
文献类型：
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作者：
Diamant, Idit;Eldar, Yonina C.;Shlomi, Tomer
细胞代谢高度依赖于环境因素，如营养素，毒素和药物，遗传因素以及两者之间的相互作用。以前的实验和计算环境因素如何影响细胞代谢的研究仅限于一小部分生长介质的分析。在这项研究中，我们提出了一种新的计算方法来预测代谢基因-营养素相互作用（GNI），揭示了基因重要性对生长培养基中营养素存在或不存在的依赖性。该方法是基于约束为基础的建模，允许系统的探索一个大的假定的生长介质的“空间”。应用该方法预测酵母氨基酸代谢系统中的GNI揭示了氨基酸生物合成途径之间复杂的相互依赖性。预测的国民总收入还能够基于基因必要性数据对培养基组成进行“反向预测”。这些结果表明，考虑到与基因致死率有关的数据，我们的方法可用于了解微生物嵌入的宿主环境，为识别物种的自然栖息地提供一种手段。
Cellular metabolism is highly dependent on environmental factors, such as nutrients, toxins and drugs, genetic factors, and interactions between the two. Previous experimental and computational studies of how environmental factors affect cellular metabolism were limited to the analysis of only a small set of growth media. In this study, we present a new computational method for predicting metabolic gene-nutrient interactions (GNI) that uncovers the dependence of gene essentiality on the presence or absence of nutrients in the growth medium. The method is based on constraint-based modeling, permitting the systematic exploration of a large putative growth media 'space'. Applying this method to predict GNIs in the amino-acid metabolism system of yeast reveals complex interdependencies between amino-acid biosynthesis pathways. The predicted GNIs also enable the 'reverse-prediction' of growth media composition, based on gene essentiality data. These results suggest that our approach may be applied to learn about the host environment in which a microorganism is embedded given data pertaining to gene lethality, providing a means for the identification of a species' natural habitat.