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Generation of Binary Tree-Child phylogenetic networks.

Gabriel Cardona1, Joan Carles Pons1, Celine Scornavacca2

  • 1Department of Mathematics and Computer Science, University of the Balearic Islands, Ctra. de Valldemossa Ctra. de Valldemossa km. 7.5. 07122 - Palma, Spain.

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Summary

This study introduces efficient methods for generating binary tree-child (BTC) networks, which model reticulate evolution. The new algorithms also enable extending evolutionary histories with new sequences.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Phylogenetics

Background:

  • Phylogenetic trees model evolutionary history but cannot represent reticulate evolution.
  • Binary tree-child (BTC) networks are a subclass of phylogenetic networks that can model reticulate evolution.
  • The combinatorial structure of BTC networks is not well understood.

Purpose of the Study:

  • To develop an efficient method for generating all possible BTC networks with a specified number of leaves.
  • To introduce reduction/augmentation operations for constructing and manipulating BTC networks.
  • To provide a method for extending existing BTC networks to include new sequences.

Main Methods:

  • Developed recursive algorithms based on reduction/augmentation operations.
  • Generalized analogous operations used for phylogenetic trees.
  • Implemented algorithms in Python for computational experiments.

Main Results:

  • Provided an efficient method for generating all BTC networks for a given number of leaves.
  • Derived a recurrence relation for an upper bound on the number of BTC networks.
  • Demonstrated the utility of the operations for extending evolutionary histories.

Conclusions:

  • The proposed reduction/augmentation operations are effective for generating and manipulating BTC networks.
  • The methods provide a foundation for further combinatorial studies of BTC networks.
  • The implemented algorithms facilitate the study of evolutionary histories involving reticulate events.