Random sampling of elementary flux modes in large-scale metabolic networks
Random sampling of elementary flux modes in large-scale metabolic networks
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DOI：
10.1093/bioinformatics/bts401
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发表时间：
2012-09-15
期刊：
影响因子：
5.8
通讯作者：
Rocha, Isabel
中科院分区：
文献类型：
--
作者：
Machado, Daniel;Soons, Zita;Rocha, Isabel
动机：代谢网络的基本（通量）模式（EM）的描述提供了一个重要的框架代谢途径分析。然而，它们在大型网络中的应用受到了模式数量组合爆炸的阻碍。在这项工作中，我们开发了一种方法，用于生成随机样本的EM不计算整个set.Results：我们的算法是一个适应的规范基础的方法，在那里我们添加了一个额外的过滤步骤，在每次迭代，选择一个随机子集的新组合的模式。为了获得无偏样本，所有候选人都被分配了相同的被选中概率。这种方法避免了计算过程中模式数量的指数增长，从而在合理的时间内生成EM的完整集合的随机样本。我们为大肠杆菌的代谢网络生成了不同大小的样本，并观察到它们保留了完整EM集的几个属性。它也表明，EM采样可以用于合理的应变设计。一个良好分布的样本，这是代表了完整的EM集，应该适合于大多数基于EM的方法进行分析和优化的代谢网络。
Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set.Results: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks.