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Related Experiment Video

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Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

Fixed-parameter tractability of the maximum agreement supertree problem.

Sylvain Guillemot¹, Vincent Berry

¹Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, Centre National de Recherche Scientifique (CNRS), University of Montpellier 2, 161 rue Ada, 34392 Montpellier, France. sguillem@lirmm.fr

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|May 1, 2010

Summary

This study analyzes the Maximum Agreement Supertree (SMAST) problem, crucial for phylogenetic supertree inference. New algorithms offer faster solutions for specific cases, improving computational efficiency in tree congruence analyses.

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Area of Science:

Computational Biology
Phylogenetics
Algorithm Analysis

Background:

The Maximum Agreement Supertree (SMAST) problem is vital for inferring evolutionary relationships and analyzing tree congruence.
It is an NP-hard problem with applications in computational phylogenetics.

Purpose of the Study:

To investigate the parameterized complexity of the SMAST problem.
To develop more efficient algorithms for specific cases of SMAST.

Main Methods:

Analysis of parameterized complexity for the SMAST problem.
Development of novel algorithms with improved time complexities.
Exploration of specific cases and parameter combinations.

Main Results:

An improved algorithm for SMAST on k rooted binary trees with time complexity O((8n)k).
Algorithms achieving O((2k)pkn2) and O(4pn3) for specific congruence conditions.
First fixed-parameter tractable algorithms for SMAST on a single parameter.

Conclusions:

The study provides significant algorithmic improvements for the SMAST problem, particularly for largely congruent input trees.
Demonstrates the potential for efficient computation in phylogenetic supertree inference.
Highlights limitations for certain parameter combinations, suggesting intractability.