Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition.
Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition.
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使用随机抽样和奇异值分解的人类红细胞中代谢调节的网络级分析。
DOI：
10.1186/1471-2105-7-132
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发表时间：
2006-03-13
影响因子：
3
通讯作者：
Palsson, BO
中科院分区：
文献类型：
--
作者：
Barrett, CL;Price, ND;Palsson, BO
通过刻画化学计量矩阵的零空间，极端通路(ExPas)被证明对于研究代谢网络的功能和能力是有价值的(S)。Expa矩阵P的奇异值分解(SVD)已被用来从网络的角度描述人类红细胞(HRBC)中的代谢调节问题。ExPas的计算是NP困难的，而对于基因组规模的网络，ExPas的计算已被证明是不可行的。因此，需要一种替代的方法来揭示基因组规模的化学计量矩阵的稳态解空间的调节性质。我们表明，hRBC代谢网络稳态解空间中随机样本形成的矩阵(W)的奇异值分解(SVD)与P的奇异值分解(SVD)得到的网络调节性质类似。这种新方法有两个主要优点。首先，它直接表示代谢溶液空间的形状，没有极端路径分布不均匀的混杂因素；第二，SVD过程可以应用于非常大量的样本，例如将从基因组规模的网络中产生的样本。这些结果表明，我们现在可以通过使用随机抽样来研究基因组规模代谢网络中调控问题的网络方面。联系人：palsson@ucsd.edu
Extreme pathways (ExPas) have been shown to be valuable for studying the functions and capabilities of metabolic networks through characterization of the null space of the stoichiometric matrix (S). Singular value decomposition (SVD) of the ExPa matrix P has previously been used to characterize the metabolic regulatory problem in the human red blood cell (hRBC) from a network perspective. The calculation of ExPas is NP-hard, and for genome-scale networks the computation of ExPas has proven to be infeasible. Therefore an alternative approach is needed to reveal regulatory properties of steady state solution spaces of genome-scale stoichiometric matrices. We show that the SVD of a matrix (W) formed of random samples from the steady-state solution space of the hRBC metabolic network gives similar insights into the regulatory properties of the network as was obtained with SVD of P. This new approach has two main advantages. First, it works with a direct representation of the shape of the metabolic solution space without the confounding factor of a non-uniform distribution of the extreme pathways and second, the SVD procedure can be applied to a very large number of samples, such as will be produced from genome-scale networks. These results show that we are now in a position to study the network aspects of the regulatory problem in genome-scale metabolic networks through the use of random sampling. Contact: palsson@ucsd.edu
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影响因子：
3.4
作者：
Beard, DA;Liang, SC;Qian, H
通讯作者：
Qian, H
影响因子：
5.8
作者：
Pfeiffer, T;Sánchez-Valdenebro, I;Schuster, S
通讯作者：
Schuster, S
影响因子：
7
作者：
Price, ND;Papin, JA;Palsson, BO
通讯作者：
Palsson, BO
影响因子：
2.9
作者：
Schilling, CH;Schuster, S;Heinrich, R
通讯作者：
Heinrich, R
DOI：
10.1073/pnas.95.8.4193
发表时间：
1998-04-14
影响因子：
11.1
作者：
Schilling, CH;Palsson, BO
通讯作者：
Palsson, BO