ArticleJournal of theoretical biology2022
A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes.
Article in Journal of theoretical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
26 citing papers in PubMed, 35 citations in OpenAlex.
- Article
- Review
- Distinct mechanisms drive post-antibiotic Tuberculosis relapse post-cure versus post-treatment-failure.bioRxiv : the preprint server for biology · 2026Article
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 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
- Timing matters in macrophage/CD4+ T cell interactions: an agent-based model comparingmSystems · 2025Article
- Control of medical digital twins with artificial neural networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Optimal control of agent-based models via surrogate modeling.PLoS computational biology · 2025Article
- GEODE: anFrontiers in pharmacology · 2025Article
- Experimental dissection of tuberculosis protective immunity: a human perspective.Frontiers in cellular and infection microbiology · 2025Review
- [Pulmonary Digital Twins].Open respiratory archives · 2024Review
- Article
- A systematic efficacy analysis of tuberculosis treatment with BPaL-containing regimens using a multiscale modeling approach.CPT: pharmacometrics & systems pharmacology · 2024Article
- "Digital twins elucidate critical role of TNPJ systems biology and applications · 2024Article
- Use of Individual-Based Mathematical Modelling to Understand More About Antibiotic Resistance Within-Host.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Toward mechanistic medical digital twins: some use cases in immunology.Frontiers in digital health · 2024Review
- Modeling and Remodeling the Cell: How Digital Twins and HCMV Can Elucidate the Complex Interactions of Viral Latency, Epigenetic Regulation, and Immune Responses.Current clinical microbiology reports · 2023Article
- Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens.PLoS computational biology · 2023Article
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), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the world's deadliest infectious diseases and remains a significant global health burden. TB disease and pathology can present clinically across a spectrum of outcomes, ranging from total sterilization of infection to active disease. Much remains unknown about the biology that drives an individual towards various clinical outcomes as it is challenging to experimentally address specific mechanisms driving clinical outcomes. Furthermore, it is unknown whether numbers of immune cells in the blood accurately reflect ongoing events during infection within human lungs. Herein, we utilize a systems biology approach by developing a whole-host model of the immune response to Mtb across multiple physiologic and time scales. This model, called HostSim, tracks events at the cellular, granuloma, organ, and host scale and represents the first whole-host, multi-scale model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. We posit that HostSim, as a first step toward personalized digital twins in TB research, offers a powerful computational tool that can be used in concert with experimental approaches to understand and predict events about various aspects of TB disease and therapeutics.
Indexed as
Identifiers
What OpenQuestion holds
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.