ArticlePLoS computational biology2016
Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 39 citations in OpenAlex.
- Machine Learning in Nonhuman Primate Models of Infectious Diseases: Current Applications and Future Perspectives.Journal of the American Association for Laboratory Animal Science : JAALAS · 2026Review
- Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden.Journal of theoretical biology · 2025Article
- Distinguishing multiple roles of T cell and macrophage involvement in determining lymph node fates during Mycobacterium tuberculosis infection.PLoS computational biology · 2025Article
- A brief overview of mathematical modeling of the within-host dynamics of Mycobacterium tuberculosis.Frontiers in applied mathematics and statistics · 2024Article
- A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons.Frontiers in systems biology · 2023Article
- A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes.Journal of theoretical biology · 2022Article
- Article
- Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis.Annual review of pharmacology and toxicology · 2021Review
- A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination.PLoS computational biology · 2020Article
- Spatial Organization and Recruitment of Non-Specific T Cells May Limit T Cell-Macrophage Interactions WithinFrontiers in immunology · 2020Article
- CD4CD8 Double Positive T cell responses during Mycobacterium tuberculosis infection in cynomolgus macaques.Journal of medical primatology · 2019Article
- Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment.Processes (Basel, Switzerland) · 2019Article
- The Role of Dimensionality in Understanding Granuloma Formation.Computation (Basel, Switzerland) · 2018Article
- Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology.Immunological reviews · 2018Review
- Integrating Non-human Primate, Human, and Mathematical Studies to Determine the Influence of BCG Timing on H56 Vaccine Outcomes.Frontiers in microbiology · 2018Article
- Heterogeneity in tuberculosis.Nature reviews. Immunology · 2017Review
- Protective immunity against tuberculosis: what does it look like and how do we find it?Current opinion in immunology · 2017Review
- Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach.PLoS computational biology · 2017Article
- 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
- Deletion of TGF-β1 Increases Bacterial Clearance by Cytotoxic T Cells in a Tuberculosis Granuloma Model.Frontiers in immunology · 2017Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2- year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identified T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. We emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.
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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.