A mathematical model of intrahost pneumococcal pneumonia infection dynamics in murine strains

Ericka Mochan1, David Swigon2, G Bard Ermentrout2

  • 1Joint Carnegie Mellon University-University of Pittsburgh, PhD Program in Computational Biology, Pittsburgh, PA 15260, USA.

Insights

This study models the immune response to pneumococcal pneumonia in mice. Differences in mouse strain responses are linked to immune response strength and bacterial clearance rates.

Area of Science:

  • Immunology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • Pneumococcal pneumonia severity varies with bacterial serotype and host mouse strain.
  • Understanding inter-strain differences in host response is crucial for disease management.

Purpose of the Study:

  • To develop an ordinary differential equation model of the intrahost immune response to bacterial pneumonia.
  • To capture and explain diverse experimentally determined responses across different murine strains.
  • To identify key factors driving inter-strain variations in pneumonia severity.

Main Methods:

  • Developed a simple ordinary differential equation model simulating intrahost dynamics.
  • Modeled bacterial populations in lungs and blood.
  • Incorporated cellular death, phagocyte activation, and immigration into the model.
  • Accounted for parameter uncertainty in model estimations.

Main Results:

  • The model successfully captures diverse experimentally observed immune responses in different mouse strains.
  • Identified key parameters influencing the host-pathogen interaction dynamics.
  • The ensemble model suggests inter-strain differences are primarily due to variations in nonspecific immune response strength and extrapulmonary phagocytosis rates.

Conclusions:

  • The ordinary differential equation model provides a framework for understanding host-pathogen dynamics in pneumococcal pneumonia.
  • Nonspecific immune response strength and extrapulmonary phagocytosis are critical determinants of inter-strain differences in pneumonia outcomes.
  • This modeling approach can aid in predicting disease severity and guiding therapeutic strategies.