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Hypergraph-based connectivity measures for signaling pathway topologies.

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We explored molecular connectivity in Reactome signaling pathways using different network models. A novel B-relaxation distance measure reveals significant pathway influences and improves functional relationship detection.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Network Science

Background:

  • Understanding cellular responses to extrinsic signals relies on pathway databases.
  • Determining molecular connectivity is crucial for analyzing signaling pathway influence.
  • Existing network representations offer different perspectives on pathway topology.

Purpose of the Study:

  • To evaluate Reactome signaling pathway connectivity across various graph representations.
  • To introduce a novel B-relaxation distance for hypergraph connectivity analysis.
  • To quantify and identify significant downstream influences between pathways.

Main Methods:

  • Analyzed Reactome pathways using graph, compound graph, bipartite graph, and hypergraph models.
  • Developed and applied the B-relaxation distance metric for hypergraph connectivity.
  • Calculated pathway influence scores based on B-relaxation distance.
  • Validated connectivity measures using STRING database interaction evidence.

Main Results:

  • Reactome pathways exhibit high connectivity as graphs but low connectivity as hypergraphs.
  • B-relaxation distance provides a tunable measure sensitive to small molecule roles.
  • Identified statistically significant downstream influences between specific Reactome pathway pairs.
  • B-connected proteins in Reactome show stronger functional relationships than bipartite connections.

Conclusions:

  • The choice of network representation significantly impacts pathway connectivity assessment.
  • B-relaxation distance offers a robust method for analyzing hypergraph-based signaling networks.
  • This approach enhances the detection of functional relationships and pathway influences.