Departments of:
Microbiology and
Immunology,
and Program in
Bioinformatics Faculty
University of Michigan
Phone: (734) 647-7722
Fax: (734) 764-7723
E-mail: kirschne@umich.edu
Department of:
Chemical Engineering
University of Michigan
Phone: (734) 763-0679
Fax: (734) 764-7453
E-mail: linderma@umich.edu
3-D Simulation of granuloma formation in the lung during M. tuberculosis infection
IMAG:
This is a
LINK
to the IMAG Wiki site that we can access for meeting
information, webinars, funding information, etc. This wiki contains information relevant to the
IMAG (Interagency Modeling and Analysis Group) and the MSM (Multi-scale Modeling Consortium).
Questions about IMAG and the MSM Consortium can be directed to Dr. Grace
Peng: penggr@mail.nih.gov
Students and Postdoctoral Fellows on this project:
Mycobacterium tuberculosis (Mtb) is the most successful pathogen
known to humans; it is responsible for ~2 million deaths/year and
infects an estimated 1/3 of the world. Despite decades of study, our
understanding of the interplay of various pathogen and immune
processes that allow for different outcomes in tuberculosis (TB),
i.e. primary TB, latency and reactivation TB, remains incomplete.
The hallmark of TB is the formation of a spherical collection of
immune cells in the lung and lymph node that both immunologically
restrains and physically contains the bacteria. Yet bacilli can
survive within granuloma for years. Current therapy requires 6 months
of treatment with multiple antibiotics; immunomodulation may be able
to augment this treatment, shortening treatment time and reducing
side effects. There is a crucial need for an in silico platform to
provide a cost-effective means of predicting the outcome of new
treatment strategies. The long-term goals of this project are to
integrate knowledge about immune system dynamics in these organs into
a realistic, multi-scale, multi-organ model of the immune response
during Mtb infection and to use this model to identify optimal
approaches for immunomodulation/antibiotic therapy. The specific aims
are: Aim 1: Incorporate new components (IL-10, bacterial population
dynamics) into our existing multi-scale lung granuloma model, and use
the model to predict factors affecting control of infection in the
lung. Aim 2: Incorporate new information (lymph node anatomy, key
cytokines, bacterial populations) into our existing multi-scale lymph
node model, and use the model to predict factors leading to
initiation of the immune response and granuloma formation and
maintenance in a lymph node. Aim 3. Build a multi-compartment,
multi-scale model that includes the models of Aims 1 and 2 and
trafficking events between the organs, and use this model to predict
infection control and pathology at the level of individual granulomas
during immunodulation/antibiotic therapy. Data generated herein from
non-human primates will inform our models and be used to validate
predictions. Our systems biology approach - incorporating both
computational and experimental tools - will allow us to predict and
test hypotheses regarding key mechanisms that influence immunity to
TB. Our interdisciplinary approach will also serve the broader
community of researchers investigating areas related to TB, immunity
and multi-scale modeling by providing data and tools that will be
made readily available.
Publications:
Bauer A,
Marino S,
Hogue I,
Kirschner D,
The effects of HIV-1 on latent TB infection,
Mathematical Models of Natural Phenomenon, Vol. 3, No. 7, 2008, pp. 229-266. PMID: n/a, PMCID: n/a, NIHMSID: n/a
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
Software Sharing Programs:
MATLAB codes and documentation for shareware analysis tools created
by our group.
To make our models "sharing-friendly", we have prepared SBML format
for the models in the papers in the following
link that are derived from this study:
