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Identification of co-evolving temporal networks.

Rasha Elhesha1, Aisharjya Sarkar1, Christina Boucher1

  • 1University of Florida, CISE Department, Gainesville, Florida, 32611, US.

BMC Genomics
|June 14, 2019
PubMed
Summary
This summary is machine-generated.

We developed Tempo, an efficient algorithm for identifying co-evolving subnetworks in temporal networks. Tempo successfully identifies age-related genes in human aging datasets, advancing network alignment beyond static models.

Keywords:
AlignmentBiologicalTemporal

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Science

Background:

  • Biological networks govern cellular functions, with temporal networks illustrating their evolution over time.
  • Understanding temporal network evolution is crucial for revealing network dynamics and resilience.
  • Co-evolving subnetworks in temporal networks exhibit similar topological changes over time.

Purpose of the Study:

  • To address the computationally challenging problem of identifying co-evolving subnetworks in pairs of temporal networks.
  • To develop a network alignment method invariant to the temporal evolution of molecular interaction networks.
  • To capture both similar network topologies and their evolution patterns.

Main Methods:

  • An efficient algorithm named Tempo was developed for identifying co-evolving subnetworks.
  • The correctness of the Tempo algorithm was formally proven.
  • Experimental validation was conducted to assess scalability and alignment quality.

Main Results:

  • Tempo demonstrates efficient scalability with network size and time points.
  • The algorithm generates statistically significant alignments even with high network evolution rates.
  • Tempo successfully identified novel genes associated with Alzheimer's, Huntington's, and Type II diabetes in a human aging dataset.

Conclusions:

  • Tempo effectively identifies age-related genes from non-age-related genes in temporal network analysis, particularly in human aging studies.
  • The Tempo algorithm represents a significant advancement in network alignment, moving beyond traditional static network models.
  • This approach enhances the study of temporal networks and their application to complex biological processes.