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DrinkwaterCharleston2016TowardsSubquadratic.pdf (1.71 MB)

Towards sub-quadratic time and space complexity solutions for the dated tree reconciliation problem

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journal contribution
posted on 2023-05-18, 21:05 authored by Drinkwater, B, Michael CharlestonMichael Charleston

Background: Recent coevolutionary analysis has considered tree topology as a means to reduce the asymptotic complexity associated with inferring the complex coevolutionary interrelationships that arise between phylogenetic trees. Targeted algorithmic design for specific tree topologies has to date been highly successful, with one recent formulation providing a logarithmic space complexity reduction for the dated tree reconciliation problem.

Methods: In this work we build on this prior analysis providing a further asymptotic space reduction, by providing a new formulation for the dynamic programming table used by a number of popular coevolutionary analysis techniques. This model gives rise to a sub quadratic running time solution for the dated tree reconciliation problem for selected tree topologies, and is shown to be, in practice, the fastest method for solving the dated tree reconciliation problem for expected evolutionary trees. This result is achieved through the analysis of not only the topology of the trees considered for coevolutionary analysis, but also the underlying structure of the dynamic programming algorithms that are traditionally applied to such analysis.

Conclusion: The newly inferred theoretical complexity bounds introduced herein are then validated using a combination of synthetic and biological data sets, where the proposed model is shown to provide an O(n) space saving, while it is observed to run in half the time compared to the fastest known algorithm for solving the dated tree reconciliation problem. What is even more significant is that the algorithm derived herein is able to guarantee the optimality of its inferred solution, something that algorithms of comparable speed have to date been unable to achieve.


Publication title

Algorithms for Molecular Biology



Article number









School of Natural Sciences


BioMed Central Ltd.

Place of publication

United Kingdom

Rights statement

© 2016 The Author(s.) Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

Repository Status

  • Open

Socio-economic Objectives

Expanding knowledge in the biological sciences

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