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Effective Boolean dynamics analysis to identify functionally important genes in large-scale signaling networks.

Hung-Cuong Trinh1, Yung-Keun Kwon1

  • 1School of Electrical Engineering, University of Ulsan, 93, Daehak-ro, Nam-gu, Ulsan 680-749, Republic of Korea.

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|August 16, 2015
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Summary

Identifying key genes for cellular development is hard. This study introduces Boolean sensitivity-based dynamics (BSU) as a novel measure, showing it effectively identifies functionally important genes in human signaling networks.

Keywords:
Boolean dynamicsBoolean sensitivityCentrality measureDrug targetsEssential genesSignaling networkUpdate-rule perturbation

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Area of Science:

  • Systems Biology
  • Genomics
  • Computational Biology

Background:

  • Identifying functionally important genes is crucial for understanding cellular development.
  • Existing structural measures have limitations; dynamics-based measures are less explored.

Purpose of the Study:

  • To investigate a dynamic measure, Boolean sensitivity-based dynamics against an update-rule perturbation (BSU), for identifying functionally important genes.
  • To compare BSU with established structural measures in human signaling networks.

Main Methods:

  • Applied BSU to two large-scale human signaling networks.
  • Analyzed evolutionary rates, essential gene proportions, and drug targets for genes with high BSU values.
  • Performed gene-ontology analysis.
  • Compared BSU's accuracy with five structural measures for identifying essential genes and drug targets.

Main Results:

  • Genes with high BSU values exhibited slower evolutionary rates and higher proportions of essential genes and drug targets.
  • Gene-ontology analysis revealed distinct functional differences between high-BSU and low-BSU gene groups.
  • BSU identified a unique set of functionally important genes, distinct from structural measures.
  • BSU demonstrated the highest synergy effect when combined with other measures.

Conclusions:

  • Boolean-sensitive dynamics (BSU) is an effective measure for identifying functionally important genes in signaling networks.
  • BSU offers complementary insights compared to structural measures and enhances gene identification through synergy.