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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Dissecting molecular network structures using a network subgraph approach.

Chien-Hung Huang1, Efendi Zaenudin2,3, Jeffrey J P Tsai2

  • 1Department of Computer Science and Information Engineering, National Formosa University, Yunlin, Taiwan.

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|October 2, 2020
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Summary
This summary is machine-generated.

This study introduces a graph-based method to analyze molecular networks, revealing underlying organizational principles and identifying specific subgraph patterns. Findings may aid in understanding cancer-related processes and driver genes.

Keywords:
Biological networksEntropyGraph theoryInformation theoryNetwork complexityNetwork motifsNetwork subgraphs

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Area of Science:

  • Systems Biology
  • Network Science
  • Bioinformatics

Background:

  • Biological functions arise from complex molecular networks.
  • Understanding these networks is crucial for deciphering biological processes.

Purpose of the Study:

  • To develop and apply a graph-based method for characterizing molecular networks.
  • To identify organizational principles and recurring patterns within biological networks.

Main Methods:

  • Decomposition of molecular networks into directed multigraphs (network subgraphs).
  • Application of spectral graph theory, reciprocity, and complexity measures (graph energy, cyclomatic complexity).
  • Analysis of 71 molecular networks across cancer, signal transduction, and cellular processes.

Main Results:

  • Identified a finite set of subgraph patterns constituting molecular networks.
  • Observed a graph energy cutoff and absence of certain subgraph patterns.
  • Found frequently observed subgraphs to be irreducible graphs.
  • Discovered enrichment of subgraph-associated driver genes in cancer-related cellular processes.

Conclusions:

  • Molecular networks exhibit underlying organizational principles.
  • The developed graph-based method provides a systematic approach to dissect biological networks.
  • Findings suggest potential applications in understanding disease mechanisms, particularly cancer.