Biological processes are based on molecular networks, which exhibit biological functions through interactions among the various genetic elements. This study presents a graph-based method to characterize molecular networks by decomposing them into directed multigraphs: network motifs. Spectral graph theory, reciprocity, and complexity measures were utilized to quantify the network motifs. It was found that graph energy, reciprocity, and cyclomatic complexity can optimally specify network motifs with some degree of degeneracy. A total of 72 molecular networks were analyzed, of three types: cancer networks, signal transduction networks, and cellular processes. It was found that molecular networks are built from a finite number of motif patterns; hence, a graph energy cutoff exists. In addition, it was found that certain motif patterns are absent from the three types of networks; hence, the Shannon entropy of the motif frequency distribution is not maximal. Furthermore, frequently found motifs are irreducible graphs. These are novel findings: they warrant further investigation and may lead to important applications. The present study provides a systematic approach for dissecting biological networks. Our discovery supports the view that there are organizational principles underlying molecular networks.
Computational analysis of molecular networks using spectral graph theory, complexity measures and information theory
Chien-Hung Huang,J. Tsai,Nilubon Kurubanjerdjit,K. Ng
Published 2019 in bioRxiv
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- Publication year
2019
- Venue
bioRxiv
- Publication date
2019-02-02
- Fields of study
Biology, Chemistry, Computer Science
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