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Updated: Jun 14, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Learning evolutionary parameters from genealogies using allelic trees.

Antoine Aragon1, Amaury Lambert2, Thierry Mora1

  • 1Laboratoire de physique de l'École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris 75005, France.

Genetics
|June 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new probabilistic method, CBA, to accurately infer cell division, degradation, and mutation rates from genetic diversity. This approach improves understanding of cellular diversification across various biological processes.

Keywords:
allelic treescell lineagesmathematical modelsphylogenetic methodspopulation geneticssomatic evolutionstatistical inference

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Area of Science:

  • Genetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Cellular diversification, driven by mutations, is crucial in development, cancer, and immune responses.
  • Understanding the genetic and growth dynamics behind cell population diversity is key to deciphering cell fate.
  • Existing phylogenetic methods have limitations in covering diverse evolutionary rates and often rely on assumptions about event timing.

Purpose of the Study:

  • To develop a novel probabilistic method for inferring cellular rates from genetic diversity.
  • To overcome limitations of current phylogenetic approaches in modeling evolutionary rates.
  • To provide insights into the timing, drivers, and outcomes of cell fates.

Main Methods:

  • Introduced CBA (Cellular Branching Analysis), a probabilistic method to infer division, degradation, and mutation rates.
  • Utilized a 'monogram' – a summarized backbone tree – for efficient phylogenetic sampling.
  • Validated the method's accuracy using simulated data.

Main Results:

  • CBA accurately infers cellular rates from observed genetic diversity.
  • The monogram structure allows for efficient sampling of phylogenies consistent with mutational data.
  • Demonstrated superior or comparable performance against standard phylogenetic methods on simulated data.

Conclusions:

  • CBA offers a more comprehensive approach to modeling cellular diversification.
  • The method enhances the understanding of genetic heterogeneity and cell fate dynamics.
  • This probabilistic framework advances the analysis of evolutionary processes in cell populations.