Elucidating host-microbe interactions in vivo by studying population dynamics using neutral genetic tags

Area of Science:

• Microbiology
• Immunology
• Systems Biology

Background:

• Host-microbe interactions are dynamic, especially during infections, with changing pathogen populations and host responses.
• Traditional methods offer limited temporal resolution, hindering the study of these complex interactions.
• Understanding these dynamics is crucial for developing effective treatments.

Purpose of the Study:

• To present novel methods for precisely quantifying host-microbe population dynamics.
• To demonstrate the application of neutral genetic tags and stochastic models in studying infections.
• To highlight the potential of these tools for predicting treatment outcomes and guiding experimental design.

Main Methods:

• Utilizing neutral genetic tags to track microbial or host cell populations over time.
• Employing stochastic population dynamic models to analyze tag composition changes.
• Integrating experimental data with mathematical modeling for quantitative insights.

Main Results:

• Enables precise quantification of biologically relevant parameters governing host-microbe interactions.
• Allows for the prediction of population dynamics under conditions like immunodeficiencies or antibiotic treatment.
• Facilitates hypothesis generation and refinement of experimental strategies.

Conclusions:

• Genetic tagging combined with dynamic modeling offers a powerful approach to study host-microbe interactions.
• These methods provide deeper insights into infection dynamics than traditional techniques.
• Future applications include personalized medicine and optimized therapeutic strategies.