Huang Chien-Hung, Zaenudin Efendi, Tsai Jeffrey J P, Kurubanjerdjit Nilubon, Dessie Eskezeia Y, Ng Ka-Lok
Department of Computer Science and Information Engineering, National Formosa University, Yunlin, Taiwan.
Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
PeerJ. 2020 Aug 6;8:e9556. doi: 10.7717/peerj.9556. eCollection 2020.
Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.
生物过程基于分子网络，这些分子网络通过遗传元件或蛋白质的相互作用展现生物功能。本研究提出了一种基于图的方法，通过将网络分解为有向多重图（即网络子图）来表征分子网络。使用谱图理论、互易性和复杂性度量来量化网络子图。图能量、互易性和圈秩复杂度能够以一定程度的简并性最优地指定网络子图。从癌症网络、信号转导网络和细胞过程这三种网络类型中分析了71个分子网络。分子网络由有限数量的子图模式构建而成，不存在具有大图能量的子图，这意味着存在图能量截止。此外，这三种网络类型中不存在某些子图模式。因此，子图频率分布的香农熵并非最大。此外，频繁观察到的子图是不可约图。这些新发现值得进一步研究，并可能带来重要应用。最后，我们观察到与癌症相关的细胞过程富含与子图相关的驱动基因。我们的研究为剖析生物网络提供了一种系统方法，并支持分子网络存在组织原则这一结论。
