Fixed-Parameter Tractable Combinatorial Algorithms for Metabolic Networks Alignments

Qiong Cheng, Jinpeng Wei, A. Zelikovsky, M. Ogihara
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Abstract

The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks is computationally challenging. Based on the property of gene duplication and function sharing in biological network, we have formulated the network alignment problem which asks the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. In this paper we present fixed parameter tractable combinatorial algorithms, which take into account the enzymes' functions and the similarity of arbitrary network topologies such as trees and arbitrary graphs wit hallowing the different types of vertex deletions. The proposed algorithms are fixed parameter tractable in the liner or square of the size of feedback vertex set respectively for the case of disallowing or allowing the deletions. We have developed the web service tool MetNetAligner which aligns metabolic networks. We evaluated our results by the randomizedP-Value computation. In the computation, we followed two standard randomization procedures and further developed two other random graph generators which keep the more stringent and consistent topology constraints. By comparing their distribution of the significant alignment pairs, we observed that the more stringent constraints in the topology the random graph generator has, the more pairs of significant alignments there exist. We also performed pair wise mapping of all pathways for four organisms and found a set of statistically significant pathway similarities. We have applied the network alignment to identifying pathway holes which are resulted by inconsistency and missing enzymes. MetNetAligner is available athttp://\\alla.cs.gsu.edu:8080/MinePW/pages/gmapping/GMMain.html \Two random graph generations and the list of identified pathway holes are available online.
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代谢网络排列的固定参数可处理组合算法
高通量基因组学和蛋白质组学数据的积累使得越来越大和复杂的代谢网络的重建成为可能。为了分析积累的数据和重建的网络,识别网络模式和代谢网络之间的进化关系是至关重要的。但即使是找到类似的网络,在计算上也是具有挑战性的。基于生物网络中基因复制和功能共享的特性,提出了允许路径收缩、顶点删除和顶点插入的最优顶点到顶点映射的网络对齐问题。在本文中,我们提出了固定参数易处理的组合算法,该算法考虑了酶的功能和任意网络拓扑(如树和任意图)的相似性,并允许不同类型的顶点删除。在不允许删除或允许删除的情况下,所提出的算法分别在反馈顶点集大小的线性或平方中具有固定参数可处理性。我们已经开发了网络服务工具MetNetAligner来校准代谢网络。我们通过随机p值计算来评估我们的结果。在计算中,我们遵循了两个标准的随机化程序,并进一步开发了另外两个保持更严格和一致的拓扑约束的随机图生成器。通过比较它们的显著对齐对分布,我们观察到随机图生成器的拓扑约束越严格,存在的显著对齐对就越多。我们还对四种生物的所有途径进行了配对映射,并发现了一组具有统计学意义的途径相似性。我们已经应用网络比对来识别由不一致和缺失酶导致的通路孔。MetNetAligner是可用的,网址://\\alla.cs.gsu.edu:8080/MinePW/pages/gmapping/GMMain.html \两个随机图形生成和已识别的路径洞列表可在线获得。
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