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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Viral Recombination

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Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
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Updated: Jun 14, 2025

Coincubation Assay for Quantifying Competitive Interactions between Vibrio fischeri Isolates
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Inference of Pairwise Interactions from Strain Frequency Data Across Settings and Context-Dependent Mutual

Thi Minh Thao Le1, Sten Madec2, Erida Gjini3

  • 1Department of Mathematics and Statistics, Masaryk University, Brno, Czech Republic.

Bulletin of Mathematical Biology
|May 21, 2025
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Summary

We developed a new method to map bacterial strain interactions using population data. This approach reveals the complex interaction network of Streptococcus pneumoniae serotypes, aiding in understanding disease dynamics and interventions.

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Area of Science:

  • Microbiology and Epidemiology
  • Mathematical Modeling
  • Population Genetics

Background:

  • Understanding microbial population dynamics requires characterizing interactions between different strains.
  • Previous methods often struggle to infer complex interaction networks from cross-sectional data.

Purpose of the Study:

  • To develop and validate a novel computational framework for estimating pairwise strain interactions from population-level frequencies.
  • To apply this method to Streptococcus pneumoniae serotype data from diverse global settings.

Main Methods:

  • Utilized replicator dynamics derived from a multi-strain SIS model with co-colonization.
  • Integrated epidemiological data on Streptococcus pneumoniae serotype frequencies from five countries.
  • Employed basic reproduction number (R0), mean global susceptibility (k), and pairwise deviations (αij) to model interactions.

Main Results:

  • Successfully inferred over 70% of the 92x92 Streptococcus pneumoniae serotype interaction matrix.
  • Demonstrated that within- and between-serotype interaction coefficients exhibit unimodal distributions.
  • Showcased the method's proof-of-concept for inferring multi-species interactions from cross-sectional data.

Conclusions:

  • The proposed framework provides a high-resolution map of pneumococcal serotype interactions.
  • This approach enables robust investigation of intervention effects in complex microbial ecosystems.
  • The method is adaptable for both cross-sectional and longitudinal data analysis.