ReviewWiley interdisciplinary reviews. Systems biology and medicine
A multifaceted approach to modeling the immune response in tuberculosis.
Review in Wiley interdisciplinary reviews. Systems biology and medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Immune dysregulation in tuberculosis-diabetes comorbidity: mechanistic and translational insights.Frontiers in immunology · 2026Pooled it
- SEIRS model for TB transmission dynamics incorporating the environment and optimal control.BMC infectious diseases · 2025Article
- Characterizing the immune response toFrontiers in immunology · 2024Review
- Article
- A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination.PLoS computational biology · 2020Article
- Current Sarcoidosis Models and the Importance of Focusing on the Granuloma.Frontiers in immunology · 2020Review
- A clinically parameterized mathematical model of Shigella immunity to inform vaccine design.PloS one · 2018Article
- A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment.Current opinion in systems biology · 2017Article
- Modeling Rabbit Responses to Single and Multiple Aerosol Exposures of Bacillus anthracis Spores.Risk analysis : an official publication of the Society for Risk Analysis · 2017Article
- Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions.Briefings in functional genomics · 2017Review
- Deletion of TGF-β1 Increases Bacterial Clearance by Cytotoxic T Cells in a Tuberculosis Granuloma Model.Frontiers in immunology · 2017Article
- A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection.Computation (Basel, Switzerland) · 2016Article
- Quantifying Limits on Replication, Death, and Quiescence of Mycobacterium tuberculosis in Mice.Frontiers in microbiology · 2016Article
- A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes.Mathematical biosciences and engineering : MBE · 2015Article
- A multi-scale approach to designing therapeutics for tuberculosis.Integrative biology : quantitative biosciences from nano to macro · 2015Article
- Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems.Cellular and molecular bioengineering · 2015Article
- Computational Studies of the Intestinal Host-Microbiota Interactome.Computation (Basel, Switzerland) · 2015Article
- Modeling tuberculosis in nonhuman primates.Cold Spring Harbor perspectives in medicine · 2014Review
- Integrative model of the immune response to a pulmonary macrophage infection: what determines the infection duration?PloS one · 2014Article
- Microenvironments in tuberculous granulomas are delineated by distinct populations of macrophage subsets and expression of nitric oxide synthase and arginase isoforms.Journal of immunology (Baltimore, Md. : 1950) · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium tuberculosis (Mtb). No available vaccine is reliable and, although treatment exists, approximately 2 million people still die each year. The hallmark of TB infection is the granuloma, a self-organizing structure of immune cells forming in the lung and lymph nodes in response to bacterial invasion. Protective immune mechanisms play a role in granuloma formation and maintenance; these act over different time/length scales (e.g., molecular, cellular, and tissue scales). The significance of specific immune factors in determining disease outcome is still poorly understood, despite incredible efforts to establish several animal systems to track infection progression and granuloma formation. Mathematical and computational modeling approaches have recently been applied to address open questions regarding host-pathogen interaction dynamics, including the immune response to Mtb infection and TB granuloma formation. This provides a unique opportunity to identify factors that are crucial to a successful outcome of infection in humans. These modeling tools not only offer an additional avenue for exploring immune dynamics at multiple biological scales but also complement and extend knowledge gained via experimental tools. We review recent modeling efforts in capturing the immune response to Mtb, emphasizing the importance of a multiorgan and multiscale approach that has tuneable resolution. Together with experimentation, systems biology has begun to unravel key factors driving granuloma formation and protective immune response in TB. WIREs Syst Biol Med 2011 3 479-489 DOI: 10.1002/wsbm.131
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.