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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

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Updated: May 21, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Exact and efficient phylodynamic simulation from arbitrarily large populations.

Michael Celentano1, William S DeWitt2, Sebastian Prillo2

  • 1Department of Statistics, University of California, Berkeley, CA 94720.

Proceedings of the National Academy of Sciences of the United States of America
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

We developed a fast tree simulation algorithm for phylodynamics. It efficiently simulates evolutionary trees from large populations, overcoming computational limits for biological studies.

Keywords:
BDMS modelefficient simulationforward-equivalent modelphylogenetic trees

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Phylogenetics

Background:

  • Biological studies often infer evolutionary history from sampled individuals, creating ascertained trees.
  • Ascertained trees are subsets of population trees, subject to biases, necessitating complex modeling.
  • Simulating these trees requires computationally intensive full population tree generation.

Purpose of the Study:

  • To develop a computationally efficient algorithm for simulating ascertained evolutionary trees.
  • To enable simulations from extremely large populations currently beyond computational reach.
  • To advance the development of novel phylodynamic inference methods.

Main Methods:

  • Proved the existence of an equivalent process with complete sampling and no death for general birth-death-mutation-sampling models.
  • Leveraged this property to develop a novel, highly efficient tree simulation algorithm.
  • The algorithm's computational cost scales linearly with the final tree size, independent of population size.

Main Results:

  • Developed a simulation algorithm that is independent of population size.
  • Achieved linear scaling with the size of the final simulated tree.
  • Enabled simulations from populations previously too large for computational analysis.

Conclusions:

  • The new algorithm dramatically reduces computational costs for simulating evolutionary trees.
  • This advancement facilitates research requiring extensive simulation data from large populations.
  • It is expected to significantly accelerate the development of new phylodynamic inference tools.