Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation.
Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation.
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DOI：
10.1186/1752-0509-3-30
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发表时间：
2009-03-06
影响因子：
--
通讯作者：
Palsson B
中科院分区：
文献类型：
--
作者：
Barrett CL;Herrgard MJ;Palsson B
新陈代谢及其调节构成了细胞内分子活动的很大一部分。细胞代谢状态的控制是由许多分子机制介导的，这些分子机制实际上将代谢网络通量状态定位在数学上可定义的稳态通量空间内的特定位置。翻译后调节构成了这些机制中的一大类，数十年的研究表明，通过翻译后代谢调节实现网络通量状态既复杂又复杂的调节问题。目前还没有提出和论证对大型生化网络中此类调节问题进行客观、自上而下评估的分析方法。我们证明，使用蒙特卡罗采样对细胞级代谢系统的稳态通量空间进行采样，并结合主成分分析和特征向量旋转，可以对系统的稳态通量状态进行低维且可生化解释的分解。这种分解以少量小反应组的形式出现，其通量变化几乎占整个系统中所有的通量变化。这一结果表明了潜在的简单性，并意味着对相对较少数量的反应集的调节可以本质上确定给定生长环境中整个网络的通量状态。我们展示了如何使用自上而下的网络分析来确定独立于特定参数和机制的关键监管要求。我们的方法补充了阐明监管机制的还原论方法，并促进我们对生物网络中全球监管策略的理解的发展。
Metabolism and its regulation constitute a large fraction of the molecular activity within cells. The control of cellular metabolic state is mediated by numerous molecular mechanisms, which in effect position the metabolic network flux state at specific locations within a mathematically-definable steady-state flux space. Post-translational regulation constitutes a large class of these mechanisms, and decades of research indicate that achieving a network flux state through post-translational metabolic regulation is both a complex and complicated regulatory problem. No analysis method for the objective, top-down assessment of such regulation problems in large biochemical networks has been presented and demonstrated. We show that the use of Monte Carlo sampling of the steady-state flux space of a cell-scale metabolic system in conjunction with Principal Component Analysis and eigenvector rotation results in a low-dimensional and biochemically interpretable decomposition of the steady flux states of the system. This decomposition comes in the form of a low number of small reaction sets whose flux variability accounts for nearly all of the flux variability in the entire system. This result indicates an underlying simplicity and implies that the regulation of a relatively low number of reaction sets can essentially determine the flux state of the entire network in the given growth environment. We demonstrate how our top-down analysis of networks can be used to determine key regulatory requirements independent of specific parameters and mechanisms. Our approach complements the reductionist approach to elucidation of regulatory mechanisms and facilitates the development of our understanding of global regulatory strategies in biological networks.
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4.8
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