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Updated: Oct 20, 2025
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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
Published on: June 11, 2015
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Stability analysis and persistence of a phage therapy model
Ei Ei Kyaw1, Hongchan Zheng1, Jingjing Wang1
1School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China.
Mathematical Biosciences and Engineering : MBE
|September 14, 2021
Summary
This study models phage therapy dynamics, analyzing bacteria-phage-immune interactions. Mathematical analysis and simulations reveal conditions for system stability and bacterial persistence, crucial for optimizing phage treatments.
Area of Science:
- Mathematical Biology
- Infectious Disease Modeling
- Immunology
Background:
- Phage therapy offers a promising alternative to antibiotics for bacterial infections.
- Understanding the complex dynamics between bacteria, bacteriophages, and the host immune system is critical for effective therapeutic strategies.
- Nonlinear interactions within these systems necessitate robust mathematical modeling to predict treatment outcomes.
Purpose of the Study:
- To analytically and numerically investigate the dynamic behavior of a mathematical model for phage therapy.
- To examine the stability of various equilibrium states, including those with and without immune responses or phages.
- To determine conditions governing the persistence and non-persistence of bacteria within the host during phage therapy.
Main Methods:
- Positivity and boundedness analysis of the dynamical system.
- Analysis of equilibrium points' existence and local asymptotic stability.
- Application of the Bendixson-Dulac criterion and Lyapunov functional method for global stability analysis.
- Numerical simulations to validate theoretical findings.
Main Results:
- Characterization of system positivity and boundedness.
- Identification and stability analysis of equilibrium solutions, including disease-free, phage-absent, and co-existence states.
- Demonstration of conditions leading to bacterial persistence or eradication based on model parameters.
- Numerical simulations confirm analytical predictions regarding system dynamics.
Conclusions:
- The study provides a comprehensive mathematical framework for understanding phage therapy dynamics.
- Stability analysis reveals critical factors influencing treatment success, such as immune response strength and phage efficacy.
- The findings offer insights into optimizing phage therapy strategies for bacterial infection control.
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