Within-Host Mathematical Models of Antibiotic Resistance

Aminat Yetunde Saula1, Gwenan Knight2, Ruth Bowness3

  • 1Department of Mathematical Sciences, University of Bath, Bath, UK.

Insights

Mathematical models enhance understanding of pathogen evolution within hosts and the global antibiotic resistance crisis. Modeling these processes aids in developing strategies to combat antimicrobial resistance genes spread by bacteria.

Area of Science:

  • Mathematical modeling
  • Infectious disease dynamics
  • Microbial evolution

Background:

  • Within-host modeling offers detailed insights into pathogen development and host immune interactions.
  • Mathematical models are crucial for understanding the global antibiotic resistance (ABR) crisis.
  • Bacteria develop resistance through mutation and horizontal gene transfer (HGT), including conjugation, transduction, and transformation.

Purpose of the Study:

  • To explore the application of mathematical models in studying within-host pathogen dynamics.
  • To investigate how mathematical models can elucidate the mechanisms of antibiotic resistance spread.
  • To identify potential strategies for combating the global ABR crisis using modeling approaches.

Main Methods:

  • Development and application of mathematical models for within-host pathogen dynamics.
  • Modeling bacterial evolution, including mutation and horizontal gene transfer (HGT) mechanisms.
  • Analysis of mobile genetic elements (MGEs) like plasmids and transposons in resistance spread.

Main Results:

  • Within-host models provide a granular view of pathogenesis and influencing factors.
  • Mathematical models can dissect the roles of HGT and MGEs in spreading antibiotic resistance.
  • Modeling facilitates a deeper understanding of bacterial adaptation and resistance mechanisms.

Conclusions:

  • Mathematical modeling is a powerful tool for studying infectious diseases and antibiotic resistance.
  • Within-host modeling enhances our comprehension of pathogen-host interactions.
  • Modeling resistance spread mechanisms can inform strategies to mitigate the ABR crisis.

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