Dissecting molecular network structures using a network subgraph approach
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. Spectra...
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doaj-905b2ec58dab49a1b141d66237abf1172020-11-25T03:11:53ZengPeerJ Inc.PeerJ2167-83592020-08-018e955610.7717/peerj.9556Dissecting molecular network structures using a network subgraph approachChien-Hung Huang0Efendi Zaenudin1Jeffrey J.P. Tsai2Nilubon Kurubanjerdjit3Eskezeia Y. Dessie4Ka-Lok Ng5Department of Computer Science and Information Engineering, National Formosa University, Yunlin, TaiwanDepartment of Bioinformatics and Medical Engineering, Asia University, Taichung, TaiwanDepartment of Bioinformatics and Medical Engineering, Asia University, Taichung, TaiwanSchool of Information Technology, Mae Fah Luang University, Chiang Rai, ThailandDepartment of Bioinformatics and Medical Engineering, Asia University, Taichung, TaiwanDepartment of Bioinformatics and Medical Engineering, Asia University, Taichung, TaiwanBiological 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.https://peerj.com/articles/9556.pdfNetwork motifsBiological networksGraph theoryInformation theoryNetwork complexityEntropy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chien-Hung Huang Efendi Zaenudin Jeffrey J.P. Tsai Nilubon Kurubanjerdjit Eskezeia Y. Dessie Ka-Lok Ng |
spellingShingle |
Chien-Hung Huang Efendi Zaenudin Jeffrey J.P. Tsai Nilubon Kurubanjerdjit Eskezeia Y. Dessie Ka-Lok Ng Dissecting molecular network structures using a network subgraph approach PeerJ Network motifs Biological networks Graph theory Information theory Network complexity Entropy |
author_facet |
Chien-Hung Huang Efendi Zaenudin Jeffrey J.P. Tsai Nilubon Kurubanjerdjit Eskezeia Y. Dessie Ka-Lok Ng |
author_sort |
Chien-Hung Huang |
title |
Dissecting molecular network structures using a network subgraph approach |
title_short |
Dissecting molecular network structures using a network subgraph approach |
title_full |
Dissecting molecular network structures using a network subgraph approach |
title_fullStr |
Dissecting molecular network structures using a network subgraph approach |
title_full_unstemmed |
Dissecting molecular network structures using a network subgraph approach |
title_sort |
dissecting molecular network structures using a network subgraph approach |
publisher |
PeerJ Inc. |
series |
PeerJ |
issn |
2167-8359 |
publishDate |
2020-08-01 |
description |
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. |
topic |
Network motifs Biological networks Graph theory Information theory Network complexity Entropy |
url |
https://peerj.com/articles/9556.pdf |
work_keys_str_mv |
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