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A Discrete Dynamical System Approach for Modeling Wolbachia Infection in Dioecious Populations.

Songül Esin1, Müge Kanuni2, Barış Özdinç3

  • 1Department of Mathematics and Computer Science, Istanbul Kultur University, Istanbul, Turkey. s.esin@iku.edu.tr.

Acta Biotheoretica
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

This study models Wolbachia infection dynamics in dioecious populations using a discrete dynamical system. The model predicts infection spread based on initial frequencies and cytoplasmic incompatibility, validated with isopod and mosquito data.

Keywords:
Cytoplasmic incompatibilityDioecious populationDiscrete dynamical systemWolbachia infection

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Area of Science:

  • Ecology
  • Population Dynamics
  • Microbiology

Background:

  • Wolbachia are intracellular bacteria that manipulate host reproduction.
  • Understanding Wolbachia spread is crucial for population control and disease vector management.
  • Dioecious populations present unique challenges for bacterial transmission dynamics.

Purpose of the Study:

  • To model the behavior of Wolbachia infection in dioecious populations.
  • To develop a recurrence relation predicting infection dynamics.
  • To compare the model with experimental data from isopods and mosquitoes.

Main Methods:

  • A discrete dynamical system approach was used.
  • A recurrence relation was derived based on initial infected frequencies and cytoplasmic incompatibility.
  • The model was validated against existing experimental data.

Main Results:

  • The recurrence relation effectively describes Wolbachia infection dynamics.
  • The model shows good agreement with data from Wolbachia-infected terrestrial isopods.
  • The system aligns with established models for Wolbachia in mosquitoes.

Conclusions:

  • The discrete dynamical system provides a robust framework for studying Wolbachia in dioecious species.
  • The model highlights the importance of initial infection frequencies and cytoplasmic incompatibility.
  • This approach offers valuable insights for predicting and managing Wolbachia infections in natural populations.