代谢途径中的随机波动

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摘要翻译：
活细胞中分子丰度的波动可能会影响其生长和健康。对于调节分子（如信号蛋白或转录因子），其表达的波动会影响网络中下游靶点的水平。在此，我们发展了一个分析框架来研究分子网络中的噪声相关现象。具体来说，我们关注的是代谢网络，它是高度关联的，噪声特性可能会限制它的结构和功能。由于线性代谢途径的动力学与精确可解的线性排队网络或传质系统的动力学之间的类比，我们得到了大量关于代谢网络中各种常见模体中中间代谢产物丰度波动的结果。除了一个病例之外，我们发现在路径的不同节点上的稳态波动是有效地不相关的。因此，酶水平的波动只影响局部性质，不会在其他地方传播到代谢网络中，中间代谢产物可以通过不同的反应自由共享。我们的方法可能适用于研究具有更复杂拓扑结构的代谢网络，或受类似生化反应控制的蛋白质信号网络。讨论了代谢数据的生物信息学分析的可能含义。
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英文标题：
《Stochastic fluctuations in metabolic pathways》
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作者：
Erel Levine and Terence Hwa
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Molecular Networks        分子网络
分类描述：Gene regulation, signal transduction, proteomics, metabolomics, gene and enzymatic networks
基因调控、信号转导、蛋白质组学、代谢组学、基因和酶网络
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一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
  Fluctuations in the abundance of molecules in the living cell may affect its growth and well being. For regulatory molecules (e.g., signaling proteins or transcription factors), fluctuations in their expression can affect the levels of downstream targets in a network. Here, we develop an analytic framework to investigate the phenomenon of noise correlation in molecular networks. Specifically, we focus on the metabolic network, which is highly inter-linked, and noise properties may constrain its structure and function. Motivated by the analogy between the dynamics of a linear metabolic pathway and that of the exactly soluable linear queueing network or, alternatively, a mass transfer system, we derive a plethora of results concerning fluctuations in the abundance of intermediate metabolites in various common motifs of the metabolic network. For all but one case examined, we find the steady-state fluctuation in different nodes of the pathways to be effectively uncorrelated. Consequently, fluctuations in enzyme levels only affect local properties and do not propagate elsewhere into metabolic networks, and intermediate metabolites can be freely shared by different reactions. Our approach may be applicable to study metabolic networks with more complex topologies, or protein signaling networks which are governed by similar biochemical reactions. Possible implications for bioinformatic analysis of metabolimic data are discussed.
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PDF链接：
https://arxiv.org/pdf/704.1667

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关键词：Fluctuations intermediate Quantitative Implications Fluctuation 框架 动力学 线性 研究 分子