Dynamic Network Modeling of Stem Cell Metabolism
Dynamic Network Modeling of Stem Cell Metabolism
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DOI：
10.1007/978-1-4939-9224-9_14
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发表时间：
2019-01-01
期刊：
影响因子：
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通讯作者：
Chandrasekaran, Sriram
中科院分区：
文献类型：
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作者：
Shen, Fangzhou;Cheek, Camden;Chandrasekaran, Sriram
干细胞代谢与干细胞的多能性和功能有着内在的联系。然而，由于代谢网络的复杂性和高度互连性，理解干细胞中的代谢重新布线一直具有挑战性。基因组规模的代谢网络模型越来越多地用于使用转录组学数据对各种细胞和组织的代谢行为进行整体建模。然而，这些建模稳态行为的强大方法对于研究动态干细胞状态转换的实用性有限。为了解决这种复杂性，我们最近开发了动态通量活动（DFA）方法; DFA是一种基因组规模的建模方法，使用时间过程代谢数据来预测代谢通量重新布线。该方案概述了分别使用转录组学和时程代谢组学数据建模稳态和动态代谢行为的步骤。使用来自幼稚和启动多能干细胞的数据，我们展示了如何使用基因组规模建模和DFA来全面表征这些状态之间的代谢差异。
Stem cell metabolism is intrinsically tied to stem cell pluripotency and function. Yet, understanding metabolic rewiring in stem cells has been challenging due to the complex and highly interconnected nature of the metabolic network. Genome-scale metabolic network models are increasingly used to holistically model the metabolic behavior of various cells and tissues using transcriptomics data. However, these powerful approaches that model steady-state behavior have limited utility for studying dynamic stem cell state transitions. To address this complexity, we recently developed the dynamic flux activity (DFA) approach; DFA is a genome-scale modeling approach that uses time-course metabolic data to predict metabolic flux rewiring. This protocol outlines the steps for modeling steady-state and dynamic metabolic behavior using transcriptomics and time-course metabolomics data, respectively. Using data from naive and primed pluripotent stem cells, we demonstrate how we can use genome-scale modeling and DFA to comprehensively characterize the metabolic differences between these states.