Dissecting molecular network structures using a network subgraph approach

PeerJ

article

Eskezeia Y. Dessie¹  Ka-Lok Ng¹  Chien-Hung Huang³  Efendi Zaenudin¹  Jeffrey J.P. Tsai¹  Nilubon Kurubanjerdjit⁵ 

[1] Department of Bioinformatics and Medical Engineering, Asia University;Department of Medical Research, China Medical University Hospital, China Medical University;Department of Computer Science and Information Engineering, National Formosa University;Research Center for Informatics, Indonesian Institute of Sciences;School of Information Technology, Mae Fah Luang University

关键词: Network motifs;    Biological networks;    Graph theory;    Information theory;    Network complexity;    Entropy;    Network subgraphs;   

DOI  :  10.7717/peerj.9556

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY