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Multi-scale Modeling Consortium grant sponsored by the National Library of Medicine of
the National Institutes of Health
A multi-scale approach for understanding antigen presentation in immunity
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Co-PIs and Collaborators:
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Dr. Jennifer Linderman,
Co-PI,
Dept of Chemical Engineering, University of
Michigan
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Dr. JoAnne Flynn, Co-PI,
Dept of Molecular Biology and Genetics,
University of Pittsburgh
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Dr. Mark Miller,
Consultant,
Dept of Pathology and Immunology, Washington University
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Dr. Debashis Ghosh, Co-PI,
Department of Biostatistics, University of
Michigan
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Students and postdoctoral Fellows on this project:
Technical support:
Project Summary:
The human immune response works to either clear or control pathogens upon
infection. Antigen presentation s critical to the immune response and is
the process by which peptide fragments of pathogens are taken up by cells
and displayed on the cell surface. Events at multiple scales (genetic
molecular, cellular, tissue, and organ) are involved in antigen
presentation. Briefly, antigen-presenting cells (ARC) take up pathogens at
the site of infection. Once they have been taken up, they are then
processed into peptides within the APC. These peptides then bind proteins
known as the major histocompatibility complex (MHC). These peptide- MHC
complexes (pMHC) are then displayed on the surface of the APC for
recognition by T cells. In addition, the dynamics of antigen presentation
and recognition are influenced by the larger tissue-level context in which
they occur, namely the structured environment of the lymph node and
ultimately by external compartmental dynamics of blood and the lymphatic
system. A comprehensive understanding of the process of antigen
presentation during an immune response will require an integrated picture
of events that are occurring over multiple spatial and time scales.
Mathematical models are tools that allow for such a multiscale
investigation. Not surprisingly, since pathogens meet APCs continually as
a first line of defense, many have evolved ways to inhibit antigen
presentation. One such intracellular bacterial pathogen is Mycobacterium
tuberculosis. Upon entering the lungs, M. tuberculosis is taken up by
resident macrophages and then replicates. To evade immune surveillance, M.
tuberculosis is known to inhibit antigen presentation of its host
macrophage. The mechanisms by which M. tuberculosis achieves this
inhibition have not been completely elucidated. Our specific aims include:
building mathematical and statistical models to: predict affinity of
peptides for different MHCII molecules with particular emphasis on the
role that peptide length plays in determining affinity; describe the
processing and the presentation events occurring in a single APC; describe
antigen recognition and some of the downstream events by capturing
interactions of cells within a single lymph node; capture relevant immune
dynamics in the body in two-compartments of blood/lymph node. Integrating
the models over multiple scales will be a key goal as well as utilizing
data from non-human primate and mouse systems. Our specific goal is to use
the models developed above towards understanding antigen presentation
during M. tuberculosis infection, the causative agent of tuberculosis, and
the leading cause of death due to infectious disease in the world today.
As the premise behind vaccines is to train the immune system to recognize
pathogens (via antigen presentation) and to quickly respond, information
gained from the studies described herein can be immediately applied to
vaccine design for M. tuberculosis as well as for other pathogens.
Publications:
1) Publications derived from this project:
Chang, ST, Ghosh, D, Kirschner, D and Linderman, JL.
Peptide
length-based prediction of peptide-MHC class II binding,
(in revisions with
Bioinformatics) June 2006.
Kirschner, DE, Chang, S, Riggs, T, Perry, N and Linderman JJ.
Toward a multi-scale model of antigen presentation
in immunity,
In:
Immunological Reviews. Eds: J. Mata and J. Cohen (in press) 2006.
Kirschner, D.
The Multi-scale immune response to pathogens: M.
tuberculosis as an example,
In In Silico Immunology, edited by J. Timmis
and D. Flower, Springer, NY (in press) 2006.
2) Publications related to this project:
Jose L. Segovia-Juarez, Suman Ganguli, and Denise Kirschner,
Identifying control mechanism of granuloma
formation during M. tuberculosis infection using an agent based
model, Journal of Theoretical Biology. 231, Issue 3,
Pages 357-376 2004.
Stewart T. Chang, Jennifer J. Linderman, and Denise E. Kirschner
A role for multiple mechanisms in the inhibition of MHC class II-
mediated antigen presentation by Mycobacterium
tuberculosis, Proceedings of the National
Academy of Sciences, USA, pps. 4530-4535
March 22, 2005 vol. 102 no. 12, 2005
David Gammack, Jose L. Segovia-Juarez, Suman Ganguli,
Simeone Marino, and Denise Kirschner,
Understanding the Immune Response in Tuberculosis
Using Different Mathematical Models and
Biological Scales,
SIAM Journal of Multiscale Modeling and Simulation,
Vol. 3, No. 2, pp. 312-345, 2005
M. Miller, A. Hejazi, S. Wei, M. Cahalan, and I. Parker,
T cell repertoire scanning is promoted
by dynamic dendritic cell behavior and random
T cell motility in the lymph node, Proc
Natl Acad Sci, vol. 101, pp. 9981003, 2004.
M. Miller, O. Safrina, I. Parker, and M. Cahalan,
Imaging the single cell dynamics of CD4 T cell
activation by dendritic cells in lymph nodes,
J Exp Med, vol. 200, pp. 847856, 2004.
M. Miller, S.Wei, M. Cahalan, and I. Parker,
Autonomous T cell trafficking examined in vivo with intravital
two-photon microscopy,
Proc Natl Acad Sci, vol. 100, pp. 26042609, 2003.
Singer, D.F., and J.J. Linderman,
Antigen Processing and Presentation: How Can Foreign Antigen
be Recognized in a Sea of Self Proteins,
J. Theor. Biol. 151: 383-404, 1991.
Modeling Methods and Tools:
Ordinary Differential Equations (several non-linear types, all continuous,
determistic approaches) and Agent-based Model (stochastic, discrete
approach)
Software Development:
Languages and Tools:
C++/C OOP, JAVA
Framework / Sharing Environment:
Open source software used in the lab, not
currently involved in development frameworks.
http://malthus.micro.med.umich.edu/MSM/shareware
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