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A new metabolomics analysis technique: Steady-state metabolic network dynamics analysis

机译:代谢组学分析新技术:稳态代谢网络动力学分析

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摘要

With the recent advances in experimental technologies, such as gas chromatography and mass spectrometry, the number of metabolites that can be measured in biofluids of individuals has markedly increased. Given a set of such measurements, a very common task encountered by biologists is to identify the metabolic mechanisms that lead to changes in the concentrations of given metabolites and interpret the metabolic consequences of the observed changes in terms of physiological problems, nutritional deficiencies, or diseases. In this paper, we present the steady-state metabolic network dynamics analysis (SMDA) approach in detail, together with its application in a cystic fibrosis study. We also present a computational performance evaluation of the SMDA tool against a mammalian metabolic network database. The query output space of the SMDA tool is exponentially large in the number of reactions of the network. However, (i) larger numbers of observations exponentially reduce the output size, and (ii) exploratory search and browsing of the query output space is provided to allow users to search for what they are looking for.
机译:随着诸如气相色谱法和质谱法之类的实验技术的最新发展,可以在个体的生物流体中测量的代谢物的数量显着增加。给定一组这样的测量值,生物学家所面临的非常普遍的任务是识别导致给定代谢物浓度发生变化的代谢机制,并从生理问题,营养缺乏或疾病方面解释观察到的变化的代谢后果。 。在本文中,我们详细介绍了稳态代谢网络动力学分析(SMDA)方法及其在囊性纤维化研究中的应用。我们还提出了针对哺乳动物代谢网络数据库的SMDA工具的计算性能评估。 SMDA工具的查询输出空间的网络反应数量成倍增加。但是,(i)数量较多的观察结果成倍地减小了输出大小,并且(ii)提供了对查询输出空间的探索性搜索和浏览,以允许用户搜索他们正在寻找的内容。

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