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Updated: Aug 9, 2026
08:57
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
Published on: August 14, 2018
Exact phylodynamic likelihood via structured Markov genealogy processes
Aaron A King1, Qianying Lin2, Edward L Ionides3
1Department of Ecology & Evolutionary Biology, Center for the Study of Complex Systems, USA; Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA.
Theoretical Population Biology
|August 7, 2026
Summary
This study introduces a new genealogy process for Markovian population models, enabling statistically efficient phylodynamic inference for a wider range of models. The method provides exact likelihood expressions and flexible simulation-based algorithms.
Area of Science:
- Mathematical Biology
- Computational Biology
- Population Genetics
Background:
- Markovian population models are fundamental in understanding biological systems.
- Phylodynamic inference typically relies on simplified models like coalescent or birth-death processes.
- Existing methods have limitations in accommodating diverse population dynamics.
Purpose of the Study:
- To develop a general framework for constructing stochastic genealogy processes from Markovian population models.
- To derive exact likelihood expressions for observed genealogies.
- To enable statistically efficient phylodynamic inference for a broader class of population models.
Main Methods:
- Construction of a unique stochastic process on genealogies for each Markovian population model.
- Derivation of exact likelihood expressions using a filter equation.
- Development of numerical algorithms for solving filter equations via simulation.
Main Results:
- Demonstration that coalescent and linear birth-death processes are special cases of the new framework.
- Derivation of properties for the novel filter equations.
- Validation of simulation-based algorithms that preserve the plug-and-play property for inference.
Conclusions:
- The developed framework significantly expands the scope of models amenable to likelihood-based phylodynamic inference.
- The new methods offer enhanced statistical efficiency and flexibility.
- This work bridges population modeling and phylogenetic inference.
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