Immune response to a variable pathogen: a stochastic model with two interlocked Darwinian entities
1Biomedical Optics Research Laboratory, Clinic of Neonatology, University Hospital Zürich, Frauenklinikstrasse 10, CH-8091 Zürich, Switzerland. c-k@gmx.ch
Computational and Mathematical Methods in Medicine
|February 21, 2013
Summary
This study models host immune responses to pathogens using a novel stochastic branching process. It reveals how pathogen mutation impacts immune cell dynamics and infection outcomes, from eradication to chronic responses.
Area of Science:
- Immunology
- Mathematical Biology
- Population Dynamics
Background:
- Host immune systems combat invading pathogens through complex cellular interactions.
- Pathogen mutations can alter antigenicity, challenging immune responses.
- Traditional modeling often uses differential equations, potentially oversimplifying stochastic biological processes.
Purpose of the Study:
- To develop a stochastic model for immune effector and memory cell populations interacting with pathogens.
- To investigate pathogen multiplication, mutation, and selection dynamics within a host.
- To explore how pathogen antigen variation influences host immune reactions.
Main Methods:
- Utilizing a Galton-Watson type branching process to model population dynamics stochastically.
- Describing pathogen populations that can change antigens via mutation during infection.
- Analyzing two model cases: pathogen eradication/chronic response and variational pathogen behavior.
Main Results:
- The stochastic model captures inherent organismal variability in population dynamics.
- In the first case, pathogens are either eradicated or elicit an oscillatory chronic immune response.
- In the second case, pathogen antigen variation leads to prolonged and complex immune reactions.
Conclusions:
- A Galton-Watson branching process provides a robust framework for modeling immune-pathogen interactions with stochasticity.
- Pathogen antigen variability is a key factor driving prolonged immune responses.
- This modeling approach offers new insights into the dynamics of infectious diseases and immune system behavior.
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