Modeling and Analysis of Yeast Sphingolipid Metabolism

描述（申请人摘要）：最近， 脂质介导的信号转导和细胞调控的研究。的 压倒性的范式集中在模块信号，其中一个刺激 调节一种酶，导致产生一种活性分子。 然而，脂质代谢的复杂性远远超过了一个简单的集合， 单独的信号传导模块，使得一种激动剂可以调节几种 酶或一种酶的生物活性产物（例如神经酰胺）可以用作药物组合物。 产生不同生物活性分子的另一种酶的底物（例如 二酰基甘油或鞘氨醇）。因此，我们假设这种复杂性 脂质代谢主要用于提供高度调节和协调的 具有不同和重叠功能的生物活性分子网络。这些 然后，网络用于整合和协调细胞的复杂反应， 各种因素和环境刺激。将对这一假设进行调查 in S.酿酒酵母，专注于基因表达反应（通过微阵列 表达），因为它们与鞘脂代谢有关。创新和 应用酵母反应的数学建模，并结合使用 生物化学和遗传学方法，根据以下具体目标：1） 预测S.酿酒酵母对 来自酵母转录反应分析的特异性刺激（表达 鞘脂代谢酶）对这些刺激的反应; 2）模拟海藻糖 代谢和糖酵解反应的热应激，并将其与 神经鞘脂代谢;和3）预测对各种神经鞘脂代谢的特异性细胞应答。 刺激的生物活性脂质的概况。这些研究将奠定 为全球模型打下基础，在这个模型中，我们可以预测特定的 由鞘脂调节的转录途径/模块，并预测 对鞘脂特定构型的总体遗传反应 程度.这个模型系统可以作为一个概念和实践 平台，将假设扩展到其他脂质类，其他方面， 调节代谢，并最终影响到哺乳动物的代谢。

DESCRIPTION (applicant's abstract): There has been a recent explosion in the study of lipid mediated signal transduction and cell regulation. The over-riding paradigm has focused on modular signaling whereby one stimulus regulates one enzyme resulting in the generation of one active molecule. However, the complexity of lipid metabolism far exceeds a simple collection of individual signaling modules such that one agonist may regulate several enzymes, or the bioactive product of one enzyme (e.g. ceramide) may serve as a substrate for another enzyme generating a different bioactive molecule (such as diacyiglycerol or sphingosine). Thus, we hypothesize that this complexity of lipid metabolism serves primarily to provide a highly regulated and coordinated network of bioactive molecules with distinct and overlapping functions. These networks then serve to integrate and coordinate complex responses of cells to various agents and environmental stimuli. This hypothesis will be investigated in S. cerevisiae, focusing on gene expression responses (by microarray expression) as they relate to sphingolipid metabolism. We will develop and apply mathematical modeling of yeast responses and use a combination of biochemical and genetic approaches according to the following specific aims: 1) predicting the bioactive sphingolipid profile of S. cerevisiae in response to specific stimuli from analysis of transcription responses of yeast (expression of enzymes of sphingolipid metabolism) to these stimuli; 2) modeling trehalose metabolism and glycolysis in response to heat stress and relate it to sphingolipid metabolism; and 3) predicting specific cell responses to various stimuli from the profile of bioactive lipids. These studies will lay the groundwork for a global model in which we may be able to predict specific transcription pathways/modules that are regulated by sphingolipids, and predict the overall genetic response to a specific configuration of sphingolipid levels. This model system could then serve as a conceptual and practical platform to extend the hypothesis to other lipid classes, other aspects of regulated metabolism, and eventually to mammalian metabolism.