Transcript Stochastic Stage-structured Modeling of the Adaptive

Stochastic Stage-structured Modeling
of the Adaptive Immune System
Dennis L. Chao1, Miles P. Davenport2,
Stephanie Forrest1, and Alan S. Perelson3
1 Department of Computer Science
2 Department of Pathology
University of New Mexico
University of New South Wales
{dlchao,forrest}@cs.unm.edu [email protected]
IEEE Computer Society
Bioinformatics Conference
Stanford, California
August 11-14, 2003
3 Theoretical Biology and Biophysics
Los Alamos National Laboratory
[email protected]
Introduction
Studying the immune system is important to:
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Understand the body's response to disease.
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Explore new treatments for disease.
Modeling as an approach to studying the immune system:
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To run experiments difficult to perform in real life.
To make observations impossible to make in the real
world.
To create variations of the system.
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Immunological models
Three approaches to immunological modeling:
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Biological
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Mathematical
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Computer
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Computer modeling
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Usually individual-based modeling.
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Build a system based on description of components (cells).
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One can directly incorporate experimental findings on cell behavior
into computer models.
The process of building a model highlights gaps and
inconsistencies in our knowledge.
But individual-based modeling is computationally
expensive...
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Stochastic stage-structured modeling
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The life cycle of a cell is divided into stages.
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All cells in a given stage are assumed to be identical.
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We need only to keep track of the number of cells in each
stage.
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Discrete time steps are used.
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Transitions between stages are stochastic.
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Level of detail captured
Characteristics of our modeling approach
Individual-based models
Stochastic stage-structured models
Mathematical models
Computational efficiency
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Discrete populations
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Stochasticity
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Computationally efficient
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Computational cost is proportional to the number of stages (100s),
not the number of cells (108).
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Cytotoxic T cells (CTLs)
A CTL killing an influenza-infected cell
Cytotoxic T cells play an important role in:
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Clearing viral infections
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Immune system response to cancer
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Tissue graft rejection
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Some autoimmune disorders
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The CTL response to virus
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Virus reaches high levels on first exposure.
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The immune response eliminates the infection.
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Immunological memory provides protective immunity in
subsequent infections by the same virus.
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Secondary response to infection is larger and faster.
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Response is specific to the pathogen.
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Individual cell behavior
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Our model requires the specification of the behavior of
individual T cells.
We would like the aggregate behavior of a population of T
cells to generate an realistic immune response.
Therefore, our model must match experiments at 2 scales:
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Cellular behavior (studies of small populations of cells)
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Immune response (studies of the immune response of organisms)
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Model T cell behavior
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When naïve cells are stimulated by pathogens, they become
effector cells.
Effector cells proliferate and eliminate infected cells.
Some effector cells become memory cells, which will
respond more quickly than naïve cells.
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The programmed response
Mercado et al., Early Programming of T cell populations responding to bacterial
infection. J Immunol 165 (12), 6833-9. 2000.
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Removing the pathogen does not affect the T cell response.
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T cells seem to have a “programmed response.”
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T cells must have state.
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Primary and secondary responses
(simulated)
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Secondary response is larger and faster than the primary.
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Virus is rapidly cleared by the secondary response.
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Secondary exposure “boosts” immunological memory.
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Programmed response
(simulated)
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Eliminating antigen does not stop the immune response.
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T cells must have state for this result.
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This phenomenon is not typically captured by mathematical
models, which are stateless.
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Future experiments
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Vaccination strategies
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Prime-boost vaccinations
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Low vs. high dose vaccinations
Effect of a mutating pathogen
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Evolution of pathogens
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Development of “escape” mutants
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The program
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A tool for immunologists and students.
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Written in Java for portability.
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Source code will be released.
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Work in progress – no user interface or documentation yet.
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Contact me ([email protected]) if you are interested.
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Summary
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The model incorporates a significant amount of experimental
data into a coherent framework.
Results agree with experiments.
The program will be available as a tool for immunological
experiments.
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Thank you!
http://www.cs.unm.edu/~dlchao
[email protected]
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