Research graph
References from The eight pillars of within-host tuberculosis modelling. Local targets link to admitted publications; unresolved targets remain external evidence.
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In the thick of it: Formation of the tuberculous granuloma and its effects on host and therapeutic responses
10.3389/fimmu.2022.820134 · 2022 · External reference
Revisiting the role of the granuloma in tuberculosis
10.1038/nri3211 · 2012 · External reference
Sterilization of granulomas is common in active and latent tuberculosis despite within-host variability in bacterial killing
10.1038/nm.3412 · 2014 · External reference
Lymph nodes—The neglected battlefield in tuberculosis
10.1371/journal.ppat.1008632 · 2020 · External reference
Hepatic tuberculosis: Myriad of hues
10.18683/germs.2021.1266 · 2021 · External reference
Advances in host-directed therapy for tuberculosis and HIV coinfection: Enhancing immune responses
10.1016/j.tim.2025.04.006 · 2025 · External reference
Immune dysregulation in tuberculosis-diabetes comorbidity: Mechanistic and translational insights
10.3389/fimmu.2026.1803046 · 2026 · External reference
Tuberculosis and diabetes mellitus: Relating immune impact of co-morbidity with challenges in disease management in high burden countries
10.1016/j.jctube.2022.100343 · 2022 · External reference
Tuberculosis in children
10.1101/cshperspect.a017855 · 2014 · External reference
Reactivation of latent tuberculosis with TNF inhibitors: Critical role of the beta 2 chain of the IL-12 receptor
10.1038/s41423-021-00694-9 · 2021 · External reference
Concomitant immunity to M. tuberculosis infection
10.1038/s41598-022-24516-8 · 2022 · External reference
Sex differences in tuberculosis
10.1007/s00281-018-0725-6 · 2019 · External reference
Disease phenotypic and geospatial features vary across genetic lineages for tuberculosis within Arkansas, 2010–2020
10.1371/journal.pgph.0001580 · 2023 · External reference
Effect of multiple genetic polymorphisms on antigen presentation and susceptibility to Mycobacterium tuberculosis infection
10.1128/iai.01677-07 · 2008 · External reference
Genetic susceptibility to tuberculosis
10.1515/cclm.2002.153 · 2002 · External reference
Genetic architecture of tuberculosis susceptibility: A comprehensive research synopsis, meta-analyses, and epidemiological evidence
10.1016/j.meegid.2022.105352 · 2022 · External reference
Experimental study of tuberculosis: From animal models to complex cell systems and organoids
10.1371/journal.ppat.1006421 · 2017 · External reference
Model systems to study Mycobacterium tuberculosis infections: An overview of scientific potential and impediments
10.3389/fcimb.2025.1572547 · 2025 · External reference
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Pathology and immune reactivity: Understanding multidimensionality in pulmonary tuberculosis
10.1007/s00281-015-0531-3 · 2016 · External reference
Evaluation of a mouse model of necrotic granuloma formation using C3HeB/FeJ mice for testing of drugs against Mycobacterium tuberculosis
10.1128/aac.00217-12 · 2012 · External reference
Ultra-low dose aerosol infection of mice with Mycobacterium tuberculosis more closely models human tuberculosis
10.1016/j.chom.2020.10.003 · 2021 · External reference
The rabbit model for assessing host-directed therapies for tuberculosis
10.1007/978-3-030-56905-1_19 · 2021 · External reference
The end of the binary era: Revisiting the spectrum of tuberculosis
10.4049/jimmunol.1800993 · 2018 · External reference
Translational research in the nonhuman primate model of tuberculosis
10.1093/ilar/ilx015 · 2017 · External reference
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An in vitro caseum binding assay that predicts drug penetration in tuberculosis lesions
10.3791/55559 · 2017 · External reference
Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis
10.1016/j.cels.2021.08.004 · 2021 · External reference
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Exploring the ethics of tuberculosis human challenge models
10.1136/jme-2023-109234 · 2023 · External reference
Animal models for tuberculosis in translational and precision medicine
10.3389/fmicb.2017.00717 · 2017 · External reference
Dynamics of co-infection with M. tuberculosis and HIV-1
10.1006/tpbi.1998.1382 · 1999 · External reference
A model to predict cell-mediated immune regulatory mechanisms during human infection with Mycobacterium tuberculosis
10.4049/jimmunol.166.3.1951 · 2001 · External reference
A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment
10.1016/j.coisb.2017.05.014 · 2017 · External reference
A brief overview of mathematical modeling of the within-host dynamics of Mycobacterium tuberculosis
10.3389/fams.2024.1355373 · 2024 · External reference
The human immune response to Mycobacterium tuberculosis in lung and lymph node
10.1016/j.jtbi.2003.11.023 · 2004 · External reference
Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model
10.1016/j.jtbi.2004.06.031 · 2004 · External reference
Multi-scale modeling predicts a balance of tumor necrosis factor-α and interleukin-10 controls the granuloma environment during Mycobacterium tuberculosis infection
10.1371/journal.pone.0068680 · 2013 · External reference
In silico evaluation and exploration of antibiotic tuberculosis treatment regimens
10.1186/s12918-015-0221-8 · 2015 · External reference
Multiscale model of Mycobacterium tuberculosis infection maps metabolite and gene perturbations to granuloma sterilization predictions
10.1128/iai.01438-15 · 2016 · External reference
Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach
10.1371/journal.pcbi.1005650 · 2017 · External reference
GEODE: An in silico tool that translates in vitro to in vivo predictions of tuberculosis antibiotic combination efficacy
10.3389/fphar.2025.1639673 · 2025 · External reference
A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs
10.1038/s41598-021-84827-0 · 2021 · External reference
A framework for multi-scale intervention modeling: Virtual cohorts, virtual clinical trials, and model-to-model comparisons
10.3389/fsysb.2023.1283341 · 2023 · External reference
Development and analysis of multiscale models for tuberculosis: From molecules to populations
2024 · External reference
The global burden of latent tuberculosis infection: A re-estimation using mathematical modelling
10.1371/journal.pmed.1002152 · 2016 · External reference
The effects of HIV-1 infection on latent tuberculosis
10.1051/mmnp:2008051 · 2008 · External reference
Development and validation of early alert model for diabetes mellitus–tuberculosis comorbidity
10.3390/microorganisms13040919 · 2025 · External reference
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Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline
10.1136/bmj.l6890 · 2020 · External reference
10.1186/s41073-019-0064-8 · 2019 · Admitted local publication
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Agent-based computer simulation and SIRS: building a bridge between basic science and clinical trials
10.1097/00024382-200116040-00006 · 2001 · External reference
Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation
10.4049/jimmunol.1003299 · 2011 · External reference
High-sensitivity MALDI-MRM-MS imaging of moxifloxacin distribution in tuberculosis-infected rabbit lungs and granulomatous lesions
10.1021/ac1029049 · 2011 · External reference
Extreme drug tolerance of Mycobacterium tuberculosis in caseum
10.1128/aac.02266-17 · 2018 · External reference
Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems
10.1007/s12195-014-0363-6 · 2014 · External reference
Spatial mapping reveals granuloma diversity and histopathological superstructure in human tuberculosis
10.1084/jem.20221392 · 2023 · External reference
Mathematical model of oxygen, nutrient, and drug transport in tuberculosis granulomas
10.1371/journal.pcbi.1011847 · 2024 · External reference
Multi-omic latent variable data integration reveals multicellular structure pathways associated with resistance to tuberculin skin test (TST)/interferon gamma release assay (IGRA) conversion in Uganda
10.1186/s12864-025-11407-1 · 2025 · External reference
Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden
10.1016/j.jtbi.2025.112176 · 2025 · External reference
A systematic efficacy analysis of tuberculosis treatment with BPaL-containing regimens using a multiscale modeling approach
10.1002/psp4.13117 · 2024 · External reference
Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle
10.1016/j.jtbi.2020.110381 · 2020 · External reference
Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: insights from a hybrid multiscale cellular automaton model
10.1016/j.jtbi.2018.03.006 · 2018 · External reference
PET CT identifies reactivation risk in cynomolgus macaques with latent M. tuberculosis
10.1371/journal.ppat.1005739 · 2016 · External reference
The population genomics of within-host Mycobacterium tuberculosis
10.1038/s41437-020-00377-7 · 2021 · External reference
Within-host population dynamics of extensively drug-resistant Mycobacterium tuberculosis revealed by an over 3-year longitudinal study
10.1093/emph/eoaf014 · 2025 · External reference
Tuberculosis detection using deep and hybrid learning techniques using X-ray images
10.1007/s00521-025-11173-x · 2025 · External reference
DAISY: a new software tool to test global identifiability of biological and physiological systems
10.1016/j.cmpb.2007.07.002 · 2007 · External reference
CaliPro: a calibration protocol that utilizes parameter density estimation to explore parameter space and calibrate complex biological models
10.1007/s12195-020-00650-z · 2020 · External reference
Optimizing tuberculosis treatment efficacy: comparing the standard regimen with moxifloxacin-containing regimens
10.1371/journal.pcbi.1010823 · 2023 · External reference
A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination
10.1371/journal.pcbi.1007280 · 2020 · External reference
Unpacking bedaquiline heteroresistance: the importance of intermediate profiles for phenotypic drug susceptibility testing
10.1128/aac.00356-25 · 2025 · External reference
Machine learning–enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations
10.1093/bib/bbae696 · 2024 · External reference
Chronic hyperglycemia drives alterations in macrophage effector function in pulmonary tuberculosis
10.1002/eji.202249839 · 2022 · External reference
The within-host evolution of antimicrobial resistance in Mycobacterium tuberculosis
10.1093/femsre/fuaa071 · 2021 · External reference
A novel machine-learning based optimization: identifying new treatment regimens for tuberculosis
10.3934/naco.2025025 · 2025 · External reference
Calibration methods to fit parameters within complex biological models
10.3389/fams.2023.1256443 · 2023 · External reference
Viewpoint of multi-omics potential in tuberculosis research: identifying biomarkers for biomanufacturing of efficient control tools
10.3389/fitd.2024.1443248 · 2024 · External reference
The evolving landscape of host biomarkers for diagnosis and monitoring of tuberculosis
10.3390/biomedicines13092076 · 2025 · External reference
Distinguishing multiple roles of T cell and macrophage involvement in determining lymph node fates during Mycobacterium tuberculosis infection
10.1371/journal.pcbi.1013033 · 2025 · External reference
The role and mechanisms of multiple immunoregulatory cells in pulmonary tuberculosis
10.3389/fimmu.2026.1841581 · 2026 · External reference
Drug-specific risk of tuberculosis in patients with rheumatoid arthritis treated with anti-TNF therapy: results from the British Society for Rheumatology Biologics Register (BSRBR)
10.1136/ard.2009.118935 · 2010 · External reference
Differential risk of tuberculosis reactivation among anti-TNF therapies is due to drug binding kinetics and permeability
10.4049/jimmunol.1103298 · 2012 · External reference
A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes
10.1016/j.jtbi.2022.111042 · 2022 · External reference
The role of dimensionality in understanding granuloma formation
10.3390/computation6040058 · 2018 · External reference
Oxygen modulates the effectiveness of granuloma mediated host response to Mycobacterium tuberculosis: a multiscale computational biology approach
10.3389/fcimb.2016.00006 · 2016 · External reference
Oxygen availability and metabolic dynamics during Mycobacterium tuberculosis latency
10.1109/tbme.2016.2605701 · 2016 · External reference
Tuneable resolution as a systems biology approach for multi‐scale, multi‐compartment computational models
10.1002/wsbm.1270 · 2014 · External reference
Digital twins in medicine
10.1038/s43588-024-00607-6 · 2024 · External reference
Immune digital twins for complex human pathologies: applications, limitations, and challenges
10.1038/s41540-024-00450-5 · 2024 · External reference
Developing the UVA/Padova Type 1 Diabetes Simulator: modeling, validation, refinements, and utility
10.1177/19322968231195081 · 2023 · External reference
Unravelling the transcriptome of the human tuberculosis lesion and its clinical implications
10.1038/s41467-025-60255-w · 2025 · External reference
Analysis of18FDG PET/CT imaging as a tool for studying Mycobacterium tuberculosis infection and treatment in non-human primates
10.3791/56375 · 2017 · External reference
Digitally barcoding Mycobacterium tuberculosis reveals in vivo infection dynamics in the macaque model of tuberculosis
10.1128/mbio.00312-17 · 2017 · External reference
Characterizing PET CT patterns and bacterial dissemination features of tuberculosis relapse in the macaque model
10.1128/iai.00177-25 · 2025 · External reference
Comprehensive analysis of Mycobacterium tuberculosis genomes reveals genetic variations in bacterial virulence
10.1016/j.chom.2024.10.004 · 2024 · External reference
Tuberculosis drug resistance profiling based on machine learning: a literature review
10.1016/j.bjid.2022.102332 · 2022 · External reference
Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against tuberculosis
10.1128/mbio.02627-19 · 2019 · External reference
Predicting drug interactions from chemogenomics using INDIGO
10.1007/978-1-4939-8891-4_13 · 2019 · External reference
Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements
10.1016/j.xcrm.2022.100737 · 2022 · External reference
Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis
10.1016/j.isci.2024.109025 · 2024 · External reference
In vitro dormancy models improve ability to predict treatment response in severe marmoset tuberculosis lesions
10.64898/2026.05.13.724664 · 2026 · External reference
Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action
10.1016/j.cels.2025.101348 · 2025 · External reference
Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth
10.1093/pnasnexus/pgae013 · 2023 · External reference
From clinical phenotypes to genomic signatures: machine learning integration for precision tuberculosis treatment prediction
10.3389/fbinf.2026.1787360 · 2026 · External reference
From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning
10.1186/s40779-023-00490-8 · 2023 · External reference
Machine learning approaches in diagnosing tuberculosis through biomarkers - a systematic review
10.1016/j.pbiomolbio.2023.03.001 · 2023 · External reference
Machine learning to design antimicrobial combination therapies: promises and pitfalls
10.1016/j.drudis.2022.04.006 · 2022 · External reference
Small data, big challenges: machine- and deep-learning strategies for data-limited drug discovery
10.1016/j.addr.2025.115762 · 2026 · External reference
Machine learning in the loop for tuberculosis diagnosis support
10.3389/fpubh.2022.876949 · 2022 · External reference
Diagnostic performance of artificial intelligence–based methods for tuberculosis detection: systematic review
10.2196/69068 · 2025 · External reference
Utilizing the heterogeneity of clinical data for model refinement and rule discovery through the application of genetic algorithms to calibrate a high-dimensional agent-based model of systemic inflammation
10.3389/fphys.2021.662845 · 2021 · External reference
Structural identifiability analysis of age-structured PDE epidemic models
10.1007/s00285-021-01711-1 · 2022 · External reference
Identifiability of parameters in mathematical models of SARS-CoV-2 infections in humans
10.1038/s41598-022-18683-x · 2022 · External reference
CrossLabFit: a novel framework for integrating qualitative and quantitative data across multiple labs for model calibration
10.1371/journal.pcbi.1013704 · 2025 · External reference
Investigating the comorbidity of COPD and tuberculosis, a computational study
10.3389/fsysb.2023.940097 · 2023 · External reference
Metabolic models, in silico trials, and algorithms
10.1089/dia.2025.18800.ac · 2025 · External reference
Chronic hyperglycemia impairs anti-microbial function of macrophages in response to Mycobacterium tuberculosis infection
10.1007/s12026-024-09462-z · 2024 · External reference
Systems biology approaches to investigate the role of granulomas in TB-HIV coinfection
10.3389/fimmu.2022.1014515 · 2022 · External reference
Macrophage dysfunction in tuberculosis–diabetes mellitus comorbidity: a scoping review of immune dysregulation and disease progression
10.3390/diabetology6050035 · 2025 · External reference
Diabetes and tuberculosis
10.1007/978-3-319-97367-8_1 · 2018 · External reference
Immunologic, metabolic and genetic impact of diabetes on tuberculosis susceptibility
10.3389/fimmu.2023.1122255 · 2023 · External reference
Recent advances in biomarkers for diabetes mellitus and tuberculosis comorbidity: a comprehensive review
10.3389/fendo.2025.1630603 · 2025 · External reference
Tuberculosis-diabetes comorbidities: mechanistic insights for clinical considerations and treatment challenges
10.4239/wjd.v15.i5.853 · 2024 · External reference
Undernutrition and tuberculosis: public health implications
10.1093/infdis/jiy675 · 2019 · External reference
Nutritional status affects immune function and exacerbates the severity of pulmonary tuberculosis
10.3389/fimmu.2024.1407813 · 2024 · External reference
Estimating the epidemiological and economic impact of providing nutritional care for tuberculosis-affected households across India: a modelling study
10.1016/s2214-109x(24)00505-9 · 2025 · External reference
Cost-effectiveness and resource implications of aggressive action on tuberculosis in China, India, and South Africa: a combined analysis of nine models
10.1016/s2214-109x(16)30265-0 · 2016 · External reference
Systematic review of mathematical models exploring the epidemiological impact of future TB vaccines
10.1080/21645515.2016.1205769 · 2016 · External reference
Contribution of CD8+ T cells to control of Mycobacterium tuberculosis infection
10.4049/jimmunol.176.7.4296 · 2006 · External reference
Strategic priming with multiple antigens can yield memory cell phenotypes optimized for infection with Mycobacterium tuberculosis: a computational study
10.3389/fmicb.2015.01477 · 2016 · External reference
Integrating non-human primate, human, and mathematical studies to determine the influence of BCG timing on H56 vaccine outcomes
10.3389/fmicb.2018.01734 · 2018 · External reference
Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques
10.1016/j.cels.2024.10.001 · 2024 · External reference
Generation of digital patients for the simulation of tuberculosis with UISS-TB
10.1186/s12859-020-03776-z · 2020 · External reference
Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB
10.1186/s12859-020-03762-5 · 2020 · External reference
Bayesian augmented clinical trials in TB therapeutic vaccination
10.3389/fmedt.2021.719380 · 2021 · External reference
Predicting the artificial immunity induced by RUTI® vaccine against tuberculosis using universal immune system simulator (UISS)
10.1186/s12859-019-3045-5 · 2019 · External reference
An agent based modeling approach for the analysis of tuberculosis – immune system dynamics
10.1109/bibm.2018.8621355 · 2018 · External reference
Universal immune system simulator framework (UISS)
10.1145/1854776.1854900 · 2010 · External reference
Vivarium: an interface and engine for integrative multiscale modeling in computational biology
10.1093/bioinformatics/btac049 · 2022 · External reference
Distilling mechanistic models from multi-omics data
10.1101/2023.09.06.556597 · 2023 · External reference
From bird viremia to bird surveillance: identifiability in a multiscale vector-borne model of Usutu virus infection
10.1101/2025.11.17.688793 · 2025 · External reference
Linked within-host and between-host models and data for infectious diseases: a systematic review
10.7717/peerj.7057 · 2019 · External reference
The FAIR guiding principles for data stewardship: fair enough
10.1038/s41431-018-0160-0 · 2018 · External reference
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
10.1038/s42256-019-0048-x · 2019 · External reference
The role of biofilms, bacterial phenotypes, and innate immune response in Mycobacterium avium colonization to infection
10.1016/j.jtbi.2021.110949 · 2022 · External reference
