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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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Uprooted Phylogenetic Networks.

P Gambette1, K T Huber2, G E Scholz3

  • 1LIGM (UMR 8049), UPEM, CNRS, ESIEE, ENPC, Université Paris-Est, 77454, Marne-la-Vallée, France.

Bulletin of Mathematical Biology
|August 2, 2017
PubMed
Summary
This summary is machine-generated.

Phylogenetic networks accommodate complex evolutionary signals. This study shows a 1-nested network can be optimally derived from its associated split system

Keywords:
Buneman graphCircular split systemClosureMedian networkPC-treesPhylogenetic network

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Phylogenetics

Background:

  • Phylogenetic trees model evolutionary history but struggle with complex signals like reticulation.
  • Phylogenetic networks generalize trees by allowing cycles, offering a more robust evolutionary model.
  • Rooted phylogenetic networks are complex; their 'uprooted' versions simplify analysis.

Purpose of the Study:

  • To simplify the understanding and analysis of rooted phylogenetic networks.
  • To establish a method for deriving 1-nested networks from split systems.
  • To explore the relationship between split systems and network structures.

Main Methods:

  • Focusing on 'uprooted' versions of phylogenetic networks.
  • Utilizing the combinatorial concept of split systems.
  • Analyzing the Buneman graph (median network) associated with a split system.

Main Results:

  • Demonstrated that a 1-nested network (N) can be obtained from the Buneman graph of its induced split system.
  • Showed this derived Buneman graph is optimal in a defined sense.
  • Established the 1-nested analogue of the splits equivalence theorem.
  • Characterized maximal circular split systems.

Conclusions:

  • The Buneman graph provides an optimal basis for constructing 1-nested phylogenetic networks.
  • This work simplifies the study of complex evolutionary histories represented by phylogenetic networks.
  • The findings contribute to a deeper combinatorial understanding of phylogenetic network structures.