Alvarez-Vasquez Fernando, Sims Kellie J, Voit Eberhard O, Hannun Yusuf A
Dept. of Biostatistics, Bioinformatics and Epidemiology. Medical University of South Carolina, Charleston, SC. USA.
Theor Biol Med Model. 2007 Oct 31;4:42. doi: 10.1186/1742-4682-4-42.
The diauxic shift in yeast requires cells to coordinate a complicated response that involves numerous genes and metabolic processes. It is unknown whether responses of this type are mediated in vivo through changes in a few "key" genes and enzymes, which are mathematically characterized by high sensitivities, or whether they are based on many small changes in genes and enzymes that are not particularly sensitive. In contrast to global assessments of changes in gene or protein interaction networks, we study here control aspects of the diauxic shift by performing a detailed analysis of one specific pathway-sphingolipid metabolism-which is known to have signaling functions and is associated with a wide variety of stress responses.
The approach uses two components: publicly available sets of expression data of sphingolipid genes and a recently developed Generalized Mass Action (GMA) mathematical model of the sphingolipid pathway. In one line of exploration, we analyze the sensitivity of the model with respect to enzyme activities, and thus gene expression. Complementary to this approach, we convert the gene expression data into changes in enzyme activities and then predict metabolic consequences by means of the mathematical model. It was found that most of the sensitivities in the model are low in magnitude, but that some stand out as relatively high. This information was then deployed to test whether the cell uses a few of the very sensitive pathway steps to mount a response or whether the control is distributed throughout the pathway. Pilot experiments confirm qualitatively and in part quantitatively the predictions of a group of metabolite simulations.
The results indicate that yeast coordinates sphingolipid mediated changes during the diauxic shift through an array of small changes in many genes and enzymes, rather than relying on a strategy involving a few select genes with high sensitivity. This study also highlights a novel approach in coupling data mining with mathematical modeling in order to evaluate specific metabolic pathways.
酵母中的二次生长转换要求细胞协调一种复杂的反应，该反应涉及众多基因和代谢过程。目前尚不清楚这种类型的反应在体内是通过少数具有高敏感性数学特征的“关键”基因和酶的变化介导的，还是基于基因和酶中许多不太敏感的小变化。与对基因或蛋白质相互作用网络变化的全局评估不同，我们在此通过对一个特定途径——鞘脂代谢进行详细分析来研究二次生长转换的控制方面，鞘脂代谢已知具有信号传导功能，并与多种应激反应相关。
该方法使用两个组件：公开可用的鞘脂基因表达数据集和最近开发的鞘脂途径广义质量作用（GMA）数学模型。在一条探索路线中，我们分析模型对酶活性以及基因表达的敏感性。作为此方法的补充，我们将基因表达数据转换为酶活性的变化，然后通过数学模型预测代谢后果。结果发现模型中的大多数敏感性在幅度上较低，但有些则相对较高。然后利用这些信息来测试细胞是使用少数非常敏感的途径步骤来产生反应，还是控制分布在整个途径中。初步实验定性且部分定量地证实了一组代谢物模拟的预测。
结果表明，酵母在二次生长转换过程中通过许多基因和酶的一系列小变化来协调鞘脂介导的变化，而不是依赖于涉及少数具有高敏感性的选定基因的策略。这项研究还突出了一种将数据挖掘与数学建模相结合以评估特定代谢途径的新方法。
