Generation of Level- k LGT Networks

Area of Science:

• Evolutionary biology
• Computational phylogenetics
• Bioinformatics

Background:

• Phylogenetic networks model species evolution beyond speciation, incorporating reticulate events like lateral gene transfer (LGT).
• LGT networks account for the asymmetry in species roles during LGT.
• The combinatorial structure of phylogenetic networks, especially LGT networks, is complex and less understood than that of phylogenetic trees.

Purpose of the Study:

• To adapt the concept of generators to LGT networks for better structural understanding.
• To introduce a novel stochastic model for generating LGT networks.
• To investigate the complexity of generated LGT networks using simulations.

Main Methods:

• Adapting the concept of level-based generators for LGT networks.
• Developing a novel two-parameter stochastic model for LGT network generation.
• Conducting computer simulations to analyze network complexity based on model parameters.

Main Results:

• Generators classified by level provide simple LGT networks, acting as building blocks for complex structures.
• A new two-parameter model successfully generates LGT networks.
• Simulations reveal insights into LGT network complexity influenced by model parameters.

Conclusions:

• Generators offer a systematic way to construct and understand LGT networks.
• The novel stochastic model facilitates the study of LGT network evolution.
• This work advances the understanding of phylogenetic network combinatorics and stochastic modeling.