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Index
1. Ketamine in brain hippocampus simulation
2. Lymphatic System Mass Transfer
3. Open Knee: A Three-Dimensional Finite Element Representation of the Knee Joint
4. Lung Model
6. Hydroxyapatite single crystals
7. Cortical information flow in Parkinson's disease
8. LymphSim
9. 2-compartment ODE multi-organ model
10. Hybrid ABM-ODE multiscale model
11. GranSim
12. GranSim-TNF
Template for Model Indexing
Revised on May 10, 2013 9:18 pm EDT by Roger Mark
The Model and Data Sharing Working Group proposes this template for new contributions to this index. Our goal is to collect searchable, useful information for many more models from MSM members. We have kept the template simple in order to encourage its use. (Please send comments and suggestions re the template to rgmark@mit.edu)
Please copy the template below into the relevant section of this index and fill it in.
MODEL TITLE:
What is being modeled? (tissue, organ, biomaterial, etc.)
Model description and purpose (including applications of public health relevance):
Check all scales for which this model may be applicable:Spatial scales:
[ ] molecular [ ] cellular [ ] tissue [ ] organ [ ] whole organism [ ] group/society
Temporal scales:
[ ] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[ ] 10-3 - 1 s [ ] 1 - 103 s [ ] hours [ ] days
[ ] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[ ] under active development
[ ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[ ] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
Has the model been validated?
If yes, how?
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set?
Keywords:
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Links: (enter URLs)
Project website:
Documentation:
Download site:
Relevant experimental data(if any):
Principal investigator:
PI contact information:
CONTRIBUTED MODELS
Multiscale modeling of cortical information flow in Parkinson's disease
MODEL TITLE: Multiscale modeling of cortical information flow in Parkinson's disease
What is being modeled? (tissue, organ, biomaterial, etc.) cortex → thalamus → basal ganglia
Model description and purpose (including applications of public health relevance): Damage to the basal ganglia leads to motor disorders such as Parkinson’s disease. But motor commands originate from the cortex, so how do basal ganglia dynamics influence cortical computation? To explore this question, we coupled a neural field model that captures the large-scale dynamics of the thalamocortical/basal ganglia system with a small-scale spiking neuronal network that captures computations in the cortex.
Check all scales for which this model may be applicable:Spatial scales:
[ ] molecular [ ] cellular [ X ] tissue [ ] organ [ ] whole organism [ ] group/society
Temporal scales:
[ ] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[ X ] 10-3 - 1 s [ X ] 1 - 103 s [ ] hours [ ] days
[ ] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[ ] under active development
[ ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[ X ] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
How has the model been validated? The model's dynamics has been compared against human electrophysiological data.
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set? no
Keywords: Parkinson's disease, neural field model, spiking neural networks, thalamus, cortex, basal ganglia, Granger causality, interlaminar processing
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Kerr CC, van Albada SJ, Neymotin SA, Chadderdon GL, Robinson PA, Lytton WW (2013). Cortical information flow in Parkinson’s disease: a composite network/field model. Front. Comput. Neurosci. 7(39):1–14. PMID: 23630492 PDF
Links: (enter URLs)
Project website: http://neurosimlab.org
Documentation: http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=147366
Download site: http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=147366 (follow Downloads link)
Principal investigator: Bill Lytton
PI contact information: billl@neurosim.downstate.edu
Hydroxyapatite single crystals
Model Title: Hydroxyapatite single crystals
What is being modeled? Single crystals of bone mineral (biomaterial)
Model description and purpose (including applications of public health relevance): Bone is a composite material with an organic matrix and inorganic minerals arranged in a hierarchy of structures spanning several length scales. At the nanometer length scale, e.g., the structure consists of self-assembled collagen fibrils and inorganic hydroxyapatite nano-crystals. The mechanical properties of the organic and inorganic phases together with their hierarchical arrangement impart bone its characteristic strength and toughness. There is therefore much interest in characterizing the mechanical properties of hydroxyapatite, not only for its importance in the overall mechanical behavior of bone in disease and in health but also for its wide-scale application in biomaterials, regenerated hard tissue, and in medicine. Hydroxyapatite is a complex molecular crystal with a hexagonal lattice structure. Purely atomistic modeling does not reflect the effects of dislocations and defects in these complex materials. We have developed a fully anisotropic nonlinear model of hydroxyapatitie crystals from nanoindentation data that includes effects of such defects at the continuum scales.
Check all scales for which this model may be applicable:Spatial scales:
[ X ] molecular [ ] cellular [ X ] tissue [ ] organ [ ] network
[ ] whole organism [ ] group/society
Temporal scales:
[ ] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[ ] 10-3 - 1 s [x ] 1 - 103 s [ x ] hours [ x ] days
[ ] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[ x ] under active development
[ x ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[ ] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
How has the model been validated? Available nanoindentation experimental data
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set? This model is athe building block for a comprehensive multiscale model of bone at the tissue and organ levels
Keywords: Hydroxyapatite, nanoindentation, molecular crystals, crystal plasticity, hard tissue, finite element model
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3123525/
Links: (enter URLs)
Project website:http://cemsim.rpi.edu/
Documentation:
Download site:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3123525/
Principal investigator: Suvranu De
PI contact information: des@rpi.edu
Ketamine in brain hippocampus simulation
MODEL TITLE: Ketamine in brain hippocampus simulation
What is being modeled? (tissue, organ, biomaterial, etc.) brain → hippocampus
Model description and purpose (including applications of public health relevance): Abnormalities in oscillations have been suggested to play a role in schizophrenia. Ketamine is an NMDA-receptor antagonist and psychotomimetic that has been used for animal models of schizophrenia. Understanding the genesis of alterations of oscillations in this disease connects the scale of psychopharmacotherapeutics to the scale of brain wave (EEG) and information processing with implications for discovery of biomarkers and development of new therapies.
Check all scales for which this model may be applicable:Spatial scales:
[ ] molecular [ ] cellular [ X ] tissue [ ] organ [ ] whole organism [ ] group/society
Temporal scales:
[ ] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[ X ] 10-3 - 1 s [ ] 1 - 103 s [ ] hours [ ] days
[ ] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[ X ] under active development
[ ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[ X ] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ X ] likely to require significant study prior to effective reuse
How has the model been validated? The model's dynamics has been compared against electrophysiological data
obtained under application of ketamine in vivo from mouse.
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set? yes, neurosimlab NEURON models
Keywords: hippocampus, schizophrenia, drug abuse, NMDA, neuronal networks, oscillations
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Neymotin SA, Lazarewicz MT, Sherif M, Contreras D, Finkel LH, Lytton WW. Ketamine disrupts theta modulation of gamma in a computer model of hippocampus. J Neurosci. 2011 31:11733-11743 PMID: 21832203 PDF
Links: (enter URLs)
Project website: http://neurosimlab.org
Documentation: http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=139421
Download site: http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=139421 (follow Downloads link)
Principal investigator: Bill Lytton
PI contact information: billl@neurosim.downstate.edu
Lymphatic System Mass Transfer
MODEL TITLE: Lymphatic System Mass Transfer
What is being modeled? (tissue, organ, biomaterial, etc.) tissue/organ
Model description and purpose (including applications of public health relevance): NA
Check all scales for which this model may be applicable:Spatial scales:
[ ] molecular [ ] cellular [ X ] tissue [ X ] organ [ ] whole organism [ ] group/society
Temporal scales:
[ ] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[ X ] 10-3 - 1 s [ X ] 1 - 103 s [ X ] hours [ ] days
[ X ] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[ X ] under active development
[ ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[ ] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ X ] likely to require significant study prior to effective reuse
How has the model been validated? No
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set? No
Keywords: Lymph, Immune, Cancer, Fluid Flow, Biomechanics
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Bertram, C.D., Macaskill, C., Moore, J.E. Jr., Simulation of a Chain of Collapsible Contracting Lymphangions with Progressive Valve Closure, ASME Journal of Biomechanical Engineering, 133 (1), 2011. PMCID: PMC3356777
Bertram, C.D., Macaskill, C., Moore, J.E. Jr., Development of a Model of a Multi-Lymphangion Lymphatic Vessel Incorporating Realistic and Measured Parameter Values, Biomechanics and Modeling in Mechanobiology, Accepted, 2013. PMID: 23801424
Wilson, J.T., Wang, W., Hellerstedt, A.H., Zawieja, D.C., Moore, J.E. Jr., Confocal Image-Based Computational Modeling of Nitric Oxide Transport in a Rat Mesenteric Lymphatic Vessel, ASME Journal of Biomechanical Engineering, 135 (5), doi:10.1115/1.4023986, 2013.
Links: (enter URLs)
Project website: NA
Documentation: NA
Download site: NA
Relevant Data:
Davis, M.J., Rahbar, E., Gashev, A.A., Zawieja, D.C., Moore, J.E. Jr., Determinants of Valve Gating in Collecting Lymphatic Vessels from Rat Mesentery, American Journal of Physiology; Heart and Circulatory Physiology, 301 (1) H48-H60, 2011. PMCID: PMC3129915
Rahbar, E., Weimer, J., Gibbs, H, Yeh, A.T., Bertram, C.D., Davis, M.J., Hill, M.A., Zawieja, D.C., Moore, J.E. Jr., Passive Pressure-Diameter Relationship and Structural Composition of Rat Mesenteric Lymphangions, Lymphatic Research and Biology, 10 (4), 152-163, 2012. PMCID: PMC3525898
Principal investigator: James E. Moore Jr
PI contact information: james.moore.jr at imperial dot ac dot uk
Lung Model
MODEL TITLE: Digital Lung
What is being modeled? (tissue, organ, biomaterial, etc.)
(1) Individual scale: pulmonary gas flow, lung tissue, heat and mass transfer, epithelial cells and nucleotide metabolism
(2) Population scale in three basic phenotypes: normal, asthmatics and chronic obstructive pulmonary disease (COPD)
Model description and purpose (including applications of public health relevance):
The lung model integrates mechanics and cell models. The mechanics model utilizes imaging-based, high-fidelity computational technologies for three-dimensional (3D) fluid and solid mechanical systems to predict airflow-induced shear stress and tissue stress at a local level in the realistic human lung models. The cell model is based upon mathematical cell biology and in vitro data for epithelial cells and nucleotide metabolism to predict adenosine triphosphate nucleotide (ATP) release, cell metabolism, ion and water transport, periciliary liquid (PCL) height, and calcium ion concentration [Ca2+].
The broad objective of this research is to apply the above model to study the mechanical force resulting from the multiscale interactions between pulmonary gas flow and lung tissue mechanics, and its role in the distribution and progression of lung disease. A driving biological hypothesis providing one motivation for this work is that lung diseases alter mechanical force, which then alters stress-mediated ATP release, disturbs PCL water homeostasis, and weakens the integrated airway defense system, forming a vicious cycle of events.
Check all scales for which this model may be applicable:Spatial scales:
[X] molecular [X] cellular [X] tissue [X] organ [ ] whole organism [ ] group/society
Temporal scales:
[X] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[ ] 10-3 - 1 s [X] 1 - 103 s [ ] hours [ ] days
[ ] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[X] under active development
[ ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[ ] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
Has the model been validated?
If yes, how?
The computational framework consists of several components, such as computational fluid dynamics, computational solid mechanics, heat and mass transfer, epithelial model and image registration for deforming airways, regional ventilation, and lung mechanics. Each of these components has been tested and validated. But more comprehensive validation of the entire integrated system has to be continuously validated.
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set?
As explained above, the computational framework consists of several components (building blocks) at individual scale. The ultimate goal is to bridge individual and population scales.
Keywords: Lung model, multiscale, computational fluid dynamics, computed tomography, image registration, tissue mechanics, epithelial model, heat and mass transfer
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Lin, C.-L., M.H. Tawhai, and E.A. Hoffman, "Multiscale image-based modeling and simulation of gas flow and particle transport in the human lungs," WIREs Systems Biology and Medicine, doi: 10.1002/wsbm.1234, 2013. PMID:23843310 http://wires.wiley.com/WileyCDA/WiresArticle/wisId-WSBM1234.html
Yin, Y., J. Choi, E. A. Hoffman, M. H. Tawhai, and C.-L. Lin, “A multiscale MDCT image-based breathing lung model with time-varing regional ventilation,” Journal of Computational Physics, 244: 168-192, 2013. PMID:23794749 http://www.sciencedirect.com/science/article/pii/S0021999112007383
Choi, S., E.A. Hoffman, S.E. Wenzel, M.H. Tawhai, Y. Yin, M. Castro, C.-L. Lin, “Registration-based Assessment of Regional Lung Function via Volumetric CT Images of Normals vs. Severe Asthmatics,” Journal of Applied Physiology, 2013 Sep;115(5):730-42. PMID: 23743399 http://www.ncbi.nlm.nih.gov/pubmed/23743399
Links:
Project website: NA
Documentation: NA
Download site: NA
Relevant experimental data(if any): NA
Principal investigator: Ching-Long Lin
PI contact information: ching-long-lin@uiowa.edu
Liver Toxicity Model
MODEL TITLE: Integrative, Mechanism Based, Multiscale Model of Liver Toxicity
What is being modeled? (tissue, organ, biomaterial, etc.)
This is a whole body model with enhanced resolution at the Liver tissue scale.
Model description and purpose (including applications of public health relevance):
Toxicity is a multiscale problem that includes effects that range from the whole body (adsorption, distribution and excretion), to tissue level effects (local dosimetry) to sub-cellular effects (metabolism, toxic outcome pathway). The current Gold Standard for toxicity determination is a combination of animal models, clinical trials in humans and "toxicology by epidemiology"(toxicity determined retrospectively after release of the chemical into the environment or after widespread use of a therapeutic agent). The NIH, EPA, FDA as well as industry and other US and OUS agencies are pushing for (1) more reliable toxicity prediction technologies, (2) more cost effective strategies for determining toxicity and (3) methods to reduce the use of animals in toxicological research. One approach to solving these challenges is to develop predictive, mechanism based, in silico models of toxicity.
Check all scales for which this model may be applicable:Spatial scales:
[X] molecular [X] cellular [X] tissue [X] organ [X] whole organism [X] group/society
Temporal scales:
[X] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[X] 10-3 - 1 s [X] 1 - 103 s [X] hours [X] days
[X] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[X] under active development
[X] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[ ] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[X] likely to require significant study prior to effective reuse
Has the model been validated?
Partially.
If yes, how?
Current validation is limited to qualitative reproduction of serum level time course data for oral dosing of Acetaminophen (a potent liver toxin) in adult human males.
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set?
This model is a set of models, including sub-models for the whole body (PBPK model in SBML), multi-cell tissue/organ level model including blood flow and diffusion, and subcelluar reaction kinetic model (RK in SBML).
Keywords:
Liver, toxicity, multiscale, PBPK, ADME, Compucell3D, SBML, Acetaminophen
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Abstract from poster presented at 2012 IMAG?MSM meeting:
The poster and the preliminary SBML and CC3D files are available at: https://comptox.indiana.edu/tox/
Links: (enter URLs)
Project website:https://comptox.indiana.edu/tox/
Documentation:https://comptox.indiana.edu/tox/
Download site:https://comptox.indiana.edu/tox/
Relevant experimental data(if any):
Principal investigator:
James P. Sluka and James A. Glazier, Indiana University
PI contact information:
jsluka --- at --- Indiana --- dot --- edu
GRANSIM
MODEL TITLE: GRANSIMTNF
What is being modeled?
Subsection of lung tissue during Mycobacterium tuberculosis infection
Model description and purpose (including applications of public health relevance):
Multiple immune factors control host responses to Mycobacterium tuberculosis infection, including the formation of granulomas, which are aggregates of immune cells whose function may reflect success or failure of the host to contain infection. We developed a multi-scale agent-based model that includes molecular, cellular, and tissue scale events that occur during granuloma formation and maintenance in lung. We use our model to identify processes that regulate TNF-a concentration and cellular behaviors and thus influence the outcome of infection within a granuloma. Furthermore, we use this computational model of a TB to determine why increased rates of tuberculosis reactivation have been reported in humans treated with TNF-a (TNF)-neutralizing drugs, and higher rates are observed with anti-TNF Abs (e.g., infliximab) as compared with TNF receptor fusion protein (etanercept).
Check all scales for which this model may be applicable:Spatial scales:
[X] molecular [X] cellular [X] tissue [ ] organ [ ] whole organism [ ] group/society
Temporal scales:
[X] <10-6 s (chemical reactions) [X] 10-6 - 10-3 s
[X] 10-3 - 1 s [X] 1 - 103 s [X] hours [X] days
[X] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[X] under active development
[X] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[X] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
Has the model been validated? Yes
If yes, how? Model has been validated against several mouse and non-human primate data sets
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set?
Keywords:
Agent-based model, multi-scale model, tumor-necrosis factor alpha, tuberculosis, chronic infection, anti-TNF antibodies
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
http://www.jimmunol.org/content/early/2011/02/14/jimmunol.1003299 PMID: 21321109
http://www.jimmunol.org/content/188/7/3169 PMID: 22379032
Links: (enter URLs)
Project website: malthus.micro.med.umich.edu/lab/
Documentation: malthus.micro.med.umich.edu/lab/
Download site: malthus.micro.med.umich.edu/lab/
Relevant experimental data(if any): malthus.micro.med.umich.edu/lab/
Principal investigator: Jennifer Linderman, Denise Kirschner, Joanne Flynn
PI contact information: linderma@umich.edu (J.J.L.) and kirschne@umich.edu (D.E.K.)
GRANSIM TNF/NFκB
MODEL TITLE: GRANSIM TNF/NFκB
What is being modeled? Subsection of lung tissue during Mycobacterium tuberculosis infection
Model description and purpose (including applications of public health relevance):
Mathematical models based on cell culture experiments have identified important molecular mechanisms controlling the dynamics of NF-κB signaling, but the dynamics of this path- way have never been studied in the context of an infection in a host. Here, we incorporate these dynamics into a virtual infection setting. We build a multi-scale model of the immune response to the pathogen Mycobacterium tuberculosis (Mtb) to explore the impact of NF-κB dynamics occurring across molecular, cellular, and tissue scales in the lung.
Check all scales for which this model may be applicable:Spatial scales:
[X] molecular [X] cellular [X] tissue [ ] organ [ ] whole organism [ ] group/society
Temporal scales:
[X] <10-6 s (chemical reactions) [X] 10-6 - 10-3 s
[X] 10-3 - 1 s [X] 1 - 103 s [X] hours [X] days
[X] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[X] under active development
[X] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[X] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
Has the model been validated? Yes
If yes, how? Model has been validated against several mouse and non-human primate data sets
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set?
Keywords: Agent-based model, multi-scale model, tumor-necrosis factor alpha, tuberculosis, chronic infection, NFκB
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Links: (enter URLs) doi: 10.3389/fphys.2012.00170PMID: 21321109
PMID: 22685435
Project website: malthus.micro.med.umich.edu/lab/
Documentation: malthus.micro.med.umich.edu/lab/
Download site: malthus.micro.med.umich.edu/lab/
Relevant experimental data(if any):
Principal investigator: Jennifer Linderman, Denise Kirschner
PI contact information: linderma@umich.edu (J.J.L.) and kirschne@umich.edu (D.E.K.)
GRANSIMTNF/IL10
MODEL TITLE: GRANSIMTNF/IL10
What is being modeled? Subsection of lung tissue during Mycobacterium tuberculosis infection
Model description and purpose (including applications of public health relevance):
Interleukin-10 (IL-10) and tumor necrosis factor-a (TNF-a) are key anti- and pro-inflammatory mediators elicited during the host immune response to Mycobacterium tuberculosis (Mtb). Understanding the opposing effects of these mediators is difficult due to the complexity of processes acting across different spatial (molecular, cellular, and tissue) and temporal (seconds to years) scales. We take an in silico approach and use multi-scale agent based modeling of the immune response to Mtb, including molecular scale details for both TNF-a and IL-10.
Check all scales for which this model may be applicable:Spatial scales:
[X] molecular [X] cellular [X] tissue [ ] organ [ ] whole organism [ ] group/society
Temporal scales:
[X] <10-6 s (chemical reactions) [X] 10-6 - 10-3 s
[X] 10-3 - 1 s [X] 1 - 103 s [X] hours [X] days
[X] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[X] under active development
[X] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[X] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
Has the model been validated? Yes
If yes, how? Model has been validated against several mouse and non-human primate data sets
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set?
Keywords: Agent-based model, multi-scale model, tumor-necrosis factor alpha, interleukin-10, tuberculosis, chronic infection
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
http://dx.plos.org/10.1371/journal.pone.0068680 PMID: 23869227
Links: (enter URLs)
Project website: malthus.micro.med.umich.edu/lab/
Documentation: malthus.micro.med.umich.edu/lab/
Download site: malthus.micro.med.umich.edu/lab/
Relevant experimental data(if any):
Principal investigator: Jennifer Linderman, Denise Kirschner
PI contact information: linderma@umich.edu (J.J.L.) and kirschne@umich.edu (D.E.K.)
LymphSim
MODEL TITLE: LymphSim
What is being modeled? Lymph Node.
Model description and purpose (including applications of public health relevance):
Our 3D agent-based cellular model of a Lymph Node that allows for the simultaneous in silico simulation of T cell trafficking, activation and production of effector cells under different antigen conditions. This systems biology approach will provide novel insights for guiding vaccine development and understanding immune responses to infection.
Check all scales for which this model may be applicable:Spatial scales:
[ ] molecular [X] cellular [X] tissue [X] organ [ ] whole organism [ ] group/society
Temporal scales:
[ ] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[ ] 10-3 - 1 s [X] 1 - 103 s [X] hours [X] days
[X] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[X] under active development
[ ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[X] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[X] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
Has the model been validated? Yes
If yes, how? This model is calibrated using cell density and short-term cell motility data from fluorescent imaging studies, and produces reasonable organ-level cell dynamics.
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set?
Potentially, this model is one compartment of our multi-organ model of Mtb-immune system interaction, which is under development.
Keywords: Agent based model, 3D , Priming , Effector T cells
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Chang Gong, Joshua T. Mattila, Mark Miller, JoAnne L. Flynn, Jennifer J. Linderman, D. Kirschner, Predicting lymph node output efficiency through systems biology, Journal of Theoretical Biology, Volume 335, October 21 2013, Pages 169-184, ePUB: June 29, 2013, DOI:10.1016/j.jtbi.2013.06.016, PMID: 23816876, PMCID: (Pending), NIHMSID: 499839
Links: (enter URLs)
Project website: http://malthus.micro.med.umich.edu/lab/movies/3dLN/
Documentation: http://malthus.micro.med.umich.edu/lab/movies/3dLN/
Download site: http://malthus.micro.med.umich.edu/lab/pubs/Gong_et_al.JTB-2013.pdf
Relevant experimental data(if any):
Principal investigator: Dr. Denise E. Kirschner
PI contact information: Denise E. Kirschner, PhD
Microbiology and Immunology, University of Michigan Medical School 6730 Medical Science Building II Ann Arbor, MI 48109-5620 Phone: (734) 647-7722 Fax: (734) 647-7723 E-mail: kirschne@umich.edu
2-compartment ODE model
MODEL TITLE: Two compartmental ODE model to study cell trafficking and T cell priming during Mtb infection
What is being modeled? Organs (Lung and Lymph Node)
Model description and purpose (including applications of public health relevance): Two compartmental model (Lung-Lymph Node) to study the immunodynamics of cells, molecules and bacteria in lung and lymph node during Mycobacterium tuberculosis infection
Check all scales for which this model may be applicable:Spatial scales:
[ ] molecular [X] cellular [ ] tissue [X] organ [ ] whole organism [ ] group/society
Temporal scales:
[ ] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[ ] 10-3 - 1 s [ ] 1 - 103 s [ ] hours [ ] days
[X] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[ ] under active development
[ ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[X] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
Has the model been validated? Yes
If yes, how? Virtual KO and depletion experiments. Murine data in the lung and lymph node.
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? No
If so, which set?
Keywords: Mathematical model; Classically and alternatively activated macrophages; DCs; Inflammation
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Simeone Marino, Amy Myers, JoAnne Flynn and Denise Kirschner, TNF and IL-10 are major factors in modulation of the phagocytic cell environment in lung and lymph node in tuberculosis: a next generation two compartmental model, Journal of Theoretical Biology, 265 (2010) 586–598, PMCID: PMC3150786, http://www.ncbi.nlm.nih.gov/pubmed/20510249?dopt=Citation
Myers, A. J., S. Marino, D. E. Kirschner, and J. L. Flynn. 2013. Inoculation dose of Mycobacterium tuberculosis does not influence priming of T cell responses in lymph nodes. J Immunol 190:4707-4716. PMCID: PMC3674545 [Available on 2014/5/1], http://www.ncbi.nlm.nih.gov/pubmed/23547119
Links: (enter URLs)
Project website: http://malthus.micro.med.umich.edu/MSM/sbml/2COMP_2010-2013
Documentation: http://malthus.micro.med.umich.edu/MSM/sbml/2COMP_2010-2013
Download site: http://malthus.micro.med.umich.edu/MSM/sbml/2COMP_2010-2013
Relevant experimental data(if any):
Principal investigator: Denise Kirschner
PI contact information: kirschne@umich.edu
hybrid ABM-ODE model
MODEL TITLE: A hybrid multi-compartment model of granuloma formation and T cell priming in Tuberculosis
What is being modeled? Organs (Lung and Lymph Node)
Model description and purpose (including applications of public health relevance):
Hybrid model to study the immunodynamics of cells, molecules and bacteria in lung (Agent-Based Model) and lymph node (Ordinary Differential Equation) during Mycobacterium tuberculosis infection
Check all scales for which this model may be applicable:Spatial scales:
[X] molecular [X] cellular [X] tissue [X] organ [ ] whole organism [ ] group/society
Temporal scales:
[X] <10-6 s (chemical reactions) [X] 10-6 - 10-3 s
[X] 10-3 - 1 s [X] 1 - 103 s [X] hours [X] days
[X] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[X] under active development
[ ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[ ] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ ] likely to require significant study prior to effective reuse
Has the model been validated? Yes
If yes, how? Virtual KO and depletion experiments. Murine data in the lung and lymph node.
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? Yes
If so, which set? GRANSIM, GRANSIM-TNF, GRANSIM-IL10
Keywords: Agent-based model; Mtb; Multi-organ models; Cell trafficking
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Marino S., Mohammed El-Kebir, Denise Kirschner. 2011. A hybrid multi-compartment model of granuloma formation and T cell priming in Tuberculosis. J Theor Biol 280:50-62 PMCID: PMC3740747 http://www.ncbi.nlm.nih.gov/pubmed/21443879?dopt=Citation
Links: (enter URLs)
Project website: http://malthus.micro.med.umich.edu/MSM/sbml/Hybrid2011
Documentation: http://malthus.micro.med.umich.edu/MSM/sbml/Hybrid2011
Download site: http://malthus.micro.med.umich.edu/MSM/sbml/Hybrid2011
Relevant experimental data(if any):
Principal investigator: Denise Kirschner
PI contact information: kirschne@umich.edu
___________________________________________________________________
SAMPLES
Sample Entry 1
Open Knee: A Three-Dimensional Finite Element Representation of the Knee Joint
MODEL TITLE: Open Knee: A Three-Dimensional Finite Element Representation of the Knee Joint
What is being modeled? (tissue, organ, biomaterial, etc.) Tibiofemoral joint and its substructures; bones, ligaments, cartilage, menisci
Model description and purpose (including applications of public health relevance): This is an illustrative model from our research program. Simulations using this model can predict tibiofemoral joint response (kinematic-kinetic) and tissue stress-strains, albeit accuracy of these predictions can be questionable. The model can be utilized to understand the mechanical function of healthy and diseased tibiofemoral joint and its tissue function. Prospective explorations of the influence of biomechanical interventions on joitn and tissue response is possible following adaptation of the model to the question of interest.
Check all scales for which this model may be applicable:Spatial scales:
[ ] molecular [ ] cellular [ X ] tissue [ X ] organ [ ] whole organism [ ] group/society
Temporal scales:
[ ] <10-6 s (chemical reactions) [ ] 10-6 - 10-3 s
[ X ] 10-3 - 1 s [ X ] 1 - 103 s [ ] hours [ ] days
[ ] weeks to months [ ] human lifetime [ ] multiple generations
This model is:
[ X ] under active development
[ X ] a demonstration or a framework to be built upon (perhaps with a sample implementation)
[ ] mature and useful in ongoing research
[ ] of historical interest (e.g., as a reference implementation of a well-known model or computational method)
[ ] likely to be usable without detailed knowledge of its internals
[ X ] likely to require significant study prior to effective reuse
How has the model been validated? Credibility is questionable, i.e., validity of the model to represent population or subject-specific kinematics-kinetics response of the tibiofemoral joint and tissue deformation metrics is not established. Yet, the model is a unique example of organ scale finite element representations in terms of its accessibility.
Is this model a member of a set of models (intended to be compatible building blocks for more elaborate models)? If so, which set?
Keywords: biomechanics, knee, continuum mechanics, joint, tissue, tibiofemoral, cartilage, ligament, meniscus, bone, finite element analysis
Key publications (1 - 3 papers that define the model, its methods and applications) (include links, PMCIDs etc.):
Links: (enter URLs)
Project website: https://simtk.org/home/openknee
Documentation: https://simtk.org/websvn/wsvn/openknee/doc/guide.pdf
Download site: https://simtk.org/home/openknee (follow Downloads link)
Principal investigator: Ahmet Erdemir
PI contact information: erdemira@ccf.org