A genome-scale metabolic network alignment method within a hypergraph-based framework using a rotational tensor-vector product.
A genome-scale metabolic network alignment method within a hypergraph-based framework using a rotational tensor-vector product.
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使用旋转张量向量积在基于超图的框架内的基因组规模代谢网络对齐方法
DOI：
10.1038/s41598-018-34692-1
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发表时间：
2018-11-06
影响因子：
4.6
通讯作者：
Xie X
中科院分区：
文献类型：
--
作者：
Shen T;Zhang Z;Chen Z;Gu D;Liang S;Xu Y;Li R;Wei Y;Liu Z;Yi Y;Xie X
生物网络比对旨在发现重要的相似性和差异性，从而找到不同生物分子网络的拓扑和/或功能组件之间的映射。然后，映射的组件可以被认为对应于它们在网络拓扑中的位置和它们的生物属性。生物网络对齐方法的发展和演变已经被这种生物网络的快速增加的可用性所加速，产生了数十种基于图论的方法的库。然而，大多数生物过程，特别是代谢反应，比简单的成对相互作用更复杂，并且包含三个或更多的参与组分。这种多边关系没有被图形捕获，并且目前缺乏克服这种限制的计算方法。本文引入超图和关联超图来分别描述代谢网络及其潜在的对齐。在这个框架内，代谢网络通过识别对称张量的最大Z-特征值来对齐。利用移位高阶幂方法来识别解。引入了旋转策略，将张量矢量积平均加速250倍，并将存储成本降低高达1,000倍。该算法在基于火花的分布式计算集群上实现，以显着提高收敛速度进一步提高50至80倍。这些参数已被探索，以了解它们对对准精度和速度的影响。特别是，初始值的选择上的稳定点的影响进行了模拟，以确保准确的逼近全局最优值。大肠杆菌MG-1655和嗜盐古菌DL 31的全基因组代谢网络的比对证实了这一框架。据我们所知，这是第一个在代谢物水平和酶水平上进行的全基因组代谢网络比对。这些结果表明，它可以提供相当多的有价值的见解代谢网络。首先，该方法可以通过代谢网络的化学演化来获取有机反应的驱动力。第二，该方法可以结合酶的化学信息和化合物的结构变化，提供新的方法来定义反应类和模块，如KEGG中的那些。第三，作为一种顶点聚焦的处理方法，该方法可以为不明确的分子提供新的结构和功能注释。相关的源代码可根据要求提供。
Biological network alignment aims to discover important similarities and differences and thus find a mapping between topological and/or functional components of different biological molecular networks. Then, the mapped components can be considered to correspond to both their places in the network topology and their biological attributes. Development and evolution of biological network alignment methods has been accelerated by the rapidly increasing availability of such biological networks, yielding a repertoire of tens of methods based upon graph theory. However, most biological processes, especially the metabolic reactions, are more sophisticated than simple pairwise interactions and contain three or more participating components. Such multi-lateral relations are not captured by graphs, and computational methods to overcome this limitation are currently lacking. This paper introduces hypergraphs and association hypergraphs to describe metabolic networks and their potential alignments, respectively. Within this framework, metabolic networks are aligned by identifying the maximal Z-eigenvalue of a symmetric tensor. A shifted higher-order power method was utilized to identify a solution. A rotational strategy has been introduced to accelerate the tensor-vector product by 250-fold on average and reduce the storage cost by up to 1,000-fold. The algorithm was implemented on a spark-based distributed computation cluster to significantly increase the convergence rate further by 50- to 80-fold. The parameters have been explored to understand their impact on alignment accuracy and speed. In particular, the influence of initial value selection on the stationary point has been simulated to ensure an accurate approximation of the global optimum. This framework was demonstrated by alignments among the genome-wide metabolic networks ofEscherichia coliMG-1655 andHalophilic archaeonDL31. To our knowledge, this is the first genome-wide metabolic network alignment at both the metabolite level and the enzyme level. These results demonstrate that it can supply quite a few valuable insights into metabolic networks. First, this method can access the driving force of organic reactions through the chemical evolution of metabolic network. Second, this method can incorporate the chemical information of enzymes and structural changes of compounds to offer new way defining reaction class and module, such as those in KEGG. Third, as a vertex-focused treatment, this method can supply novel structural and functional annotation for ill-defined molecules. The related source code is available on request.
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DOI：
10.1504/ijbra.2013.054688
发表时间：
2013-01-01
影响因子：
--
作者：
Berlo, Rogier J P Van;Winterbach, Wynand;Ridder, Dick De
通讯作者：
Ridder, Dick De
DOI：
10.1137/s0895479801387413
发表时间：
2002-03-06
影响因子：
1.5
作者：
Kofidis, E;Regalia, PA
通讯作者：
Regalia, PA
影响因子：
2.4
作者：
Michoel, Tom;Nachtergaele, Bruno
通讯作者：
Nachtergaele, Bruno
DOI：
10.1109/tpami.2011.110
发表时间：
2011-12-01
影响因子：
23.6
作者：
Duchenne, Olivier;Bach, Francis;Ponce, Jean
通讯作者：
Ponce, Jean
卤素古细菌的代谢。
影响因子：
2.9
作者：
Falb, Michaela;Mueller, Kerstin;Koenigsmaier, Lisa;Oberwinkler, Tanja;Horn, Patrick;von Gronau, Susanne;Gonzalez, Orland;Pfeiffer, Friedhelm;Bornberg-Bauer, Erich;Oesterhelt, Dieter
通讯作者：
Oesterhelt, Dieter