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Edge-based sensitivity analysis of signaling networks by using Boolean dynamics.

Hung-Cuong Trinh1, Yung-Keun Kwon1

  • 1School of Electrical Engineering, University of Ulsan, 93 Daehak-Ro, Ulsan, 44610 Nam -Gu, Republic of Korea.

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This study introduces an edgetic sensitivity measure for biological networks, revealing that sensitive edges often link drug-target genes and outperform node-based sensitivity in predicting therapeutic targets. These findings offer valuable insights into network dynamics and potential drug discovery.

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Area of Science:

  • Systems Biology
  • Network Science
  • Computational Biology

Background:

  • Biological networks model molecular components and interactions.
  • Previous research focused on node mutations, neglecting edge mutations.
  • Edgetic mutations, affecting interactions, are understudied.

Purpose of the Study:

  • Define and quantify edgetic sensitivity in Boolean network models.
  • Investigate properties of highly sensitive edges in random and real signaling networks.
  • Evaluate edgetic sensitivity for predicting drug targets.

Main Methods:

  • Defined an edgetic sensitivity measure for edge-removal mutations.
  • Conducted extensive simulations on random and human signaling networks.
  • Analyzed structural characteristics and gene-ontology enrichments of sensitive edges.
  • Validated findings using p53 cancer and T-cell apoptosis networks.

Main Results:

  • Sensitive edges in random networks link nodes susceptible to knockout.
  • Sensitive edges in human signaling networks connect potential drug-target genes.
  • Edgetic sensitivity predicts drug targets more effectively than node-based sensitivity.
  • Highly sensitive edges exhibit distinct structural properties (low connectivity, feedback loops, high betweenness) and gene ontology enrichments.
  • Genes associated with sensitive interactions form central connected components.

Conclusions:

  • Edgetic sensitivity is a valuable measure for understanding biological network dynamics.
  • Highly sensitive interactions represent promising edgetic drug targets.
  • This approach enhances the identification of therapeutic strategies in complex signaling pathways.