Over the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treatments. Before the field can reach this point, however, sufficient tools for integrating and analyzing the ever-growing availability of lipidomics data will need to be developed. To this end, genome-scale models (GEMs) of metabolic networks are useful tools, though their large size and complexity introduces too much uncertainty in the accuracy of predicted outcomes. Ideally, therefore, a model for studying lipids would contain only the pathways required for the proper analysis of these biomolecules, but would not be an ad hoc reduction. We hereby present a metabolic model that focuses on lipid metabolism constructed through the integration of detailed lipid pathways into an already existing GEM of Saccharomyces cerevisiae. Our model was then systematically reduced around the subsystems defined by these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs regarding the focus and detail on the lipid metabolism, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions.

中文翻译：

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redLips：酵母脂质代谢网络的综合机制模型。

在过去的几十年中，酵母已成为研究脂质生物化学的关键模型生物。由于脂质的调节与各种生理病理学密切相关，因此对这些生物分子的研究可能会带来新的诊断和治疗方法。但是，在该领域到达这一点之前，将需要开发出足够的工具来集成和分析脂质组学数据的日益增长的可用性。为此，代谢网络的基因组规模模型（GEM）是有用的工具，尽管它们的庞大和复杂性给预测结果的准确性带来了太多不确定性。因此，理想地，用于研究脂质的模型将仅包含对这些生物分子进行适当分析所需的途径，而不会是特别的减少。我们在此提出一种代谢模型，其重点是通过将详细的脂质途径整合到酿酒酵母已经存在的GEM中而构建的脂质代谢。然后，围绕这些途径定义的子系统系统地缩小了我们的模型，从而为复杂的研究提供了更易于管理的模型大小。我们显示该模型与其他酵母GEM在脂质代谢上的关注和细节一样一致且包容，并且可以用作整合脂质组学数据的支架，以改善对脂质相关生物学功能的预测。然后，围绕这些途径定义的子系统系统地缩小了我们的模型，从而为复杂的研究提供了更易于管理的模型大小。我们显示该模型与其他酵母GEM在脂质代谢上的关注和细节一样一致且包容，并且可以用作整合脂质组学数据的支架，以改善脂质相关生物学功能研究中的预测。然后，围绕这些途径定义的子系统系统地缩小了我们的模型，从而为复杂的研究提供了更易于管理的模型大小。我们显示该模型与其他酵母GEM在脂质代谢上的关注和细节一样一致且包容，并且可以用作整合脂质组学数据的支架，以改善脂质相关生物学功能研究中的预测。