Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

Title: A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment

Abstract

While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. In this paper, we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areasmore » non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. In conclusion, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [5];  [7];  [2]
+ Show Author Affiliations
  1. Univ. of Michigan, Ann Arbor, MI (United States); Univ. of Michigan Medical School, Ann Arbor, MI (United States)
  2. Univ. of Michigan, Ann Arbor, MI (United States)
  3. Children's Hospital of Pittsburgh of the Univ. of Pittsburgh Medical Center, Pittsburgh, PA (United States)
  4. The State Univ. of New Jersey, Newark, NJ (United States)
  5. Univ. of Pittsburgh, Pittsburgh, PA (United States)
  6. Adventist Univ. of Health Sciences, Orlando, FL (United States)
  7. Univ. of Michigan Medical School, Ann Arbor, MI (United States)
Publication Date:
Research Org.:
Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1344478
Alternate Identifier(s):
OSTI ID: 1243356
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Theoretical Biology
Additional Journal Information:
Journal Volume: 367; Journal ID: ISSN 0022-5193
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 97 MATHEMATICS AND COMPUTING; pharmacodynamics; pharmacokinetics; agent based model; granuloma; antibiotic gradients

Citation Formats

Pienaar, Elsje, Cilfone, Nicholas A., Lin, Philana Ling, Dartois, Veronique, Mattila, Joshua T., Butler, J. Russell, Flynn, JoAnne L., Kirschner, Denise E., and Linderman, Jennifer J. A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment. United States: N. p., 2014. Web. doi:10.1016/j.jtbi.2014.11.021.
Copy to clipboard
Pienaar, Elsje, Cilfone, Nicholas A., Lin, Philana Ling, Dartois, Veronique, Mattila, Joshua T., Butler, J. Russell, Flynn, JoAnne L., Kirschner, Denise E., & Linderman, Jennifer J. A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment. United States. https://doi.org/10.1016/j.jtbi.2014.11.021
Copy to clipboard
Pienaar, Elsje, Cilfone, Nicholas A., Lin, Philana Ling, Dartois, Veronique, Mattila, Joshua T., Butler, J. Russell, Flynn, JoAnne L., Kirschner, Denise E., and Linderman, Jennifer J. Mon . "A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment". United States. https://doi.org/10.1016/j.jtbi.2014.11.021. https://www.osti.gov/servlets/purl/1344478.
Copy to clipboard
@article{osti_1344478,
title = {A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment},
author = {Pienaar, Elsje and Cilfone, Nicholas A. and Lin, Philana Ling and Dartois, Veronique and Mattila, Joshua T. and Butler, J. Russell and Flynn, JoAnne L. and Kirschner, Denise E. and Linderman, Jennifer J.},
abstractNote = {While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. In this paper, we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. In conclusion, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.},
doi = {10.1016/j.jtbi.2014.11.021},
journal = {Journal of Theoretical Biology},
number = ,
volume = 367,
place = {United States},
year = {Mon Dec 08 00:00:00 EST 2014},
month = {Mon Dec 08 00:00:00 EST 2014}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.jtbi.2014.11.021
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Citation Metrics:
Cited by: 44 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1 Figure 1: Model structure. (A) Tissue pharmacokinetics (PK) are added to the existing granuloma model (GranSim) by accounting for antibiotic permeability through vascular walls, diffusion in tissue, uptake by host cells, and degradation by host cells and bacteria. (B) Plasma PK is modeled using two transit compartments, a plasma compartmentmore » and a peripheral compartment. The peripheral compartment represents other tissues and organs. Antibiotic doses are added to the first transit compartment. Antibiotic dynamics in the plasma compartment are characterized using the metrics indicated in the bottom panel. (C) Pharmacodynamics are implemented using Emax models, defined by maximum activity (Emax), concentration where 50% of maximum activity is achieved (C50), and Hill constant (H) describing steepness of the curve. We define PD parameters separately for bacterial subpopulations, since different subpopulations have been shown to have different susceptibilities to INH and RIF. We define Emax and C50 for each antibiotic and bacterial subpopulation combination. ka: absorption rate constant; $\mathcal{Q}$: inter-compartmental clearance rate constant; CL: clearance rate constant from plasma; Cp: plasma antibiotic concentration; Cmax: maximum concentration; AUC: area under the curve; tmax: time after dosing when maximal concentration is reached; MIC: minimum inhibitory concentration.« less
All figures and tables (7 total)
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

The spectrum of latent tuberculosis: rethinking the biology and intervention strategies
journal, October 2009

  • Barry, Clifton E.; Boshoff, Helena I.; Dartois, Véronique
  • Nature Reviews Microbiology, Vol. 7, Issue 12
  • DOI: 10.1038/nrmicro2236

American Thoracic Society/Centers for Disease Control and Prevention/Infectious Diseases Society of America Treatment of Tuberculosis
journal, February 2003

  • ,
  • American Journal of Respiratory and Critical Care Medicine, Vol. 167, Issue 4, p. 603-662
  • DOI: 10.1164/rccm.167.4.603

Tuberculosis — metabolism and respiration in the absence of growth
journal, January 2005

  • Boshoff, Helena I. M.; Barry, Clifton E.
  • Nature Reviews Microbiology, Vol. 3, Issue 1
  • DOI: 10.1038/nrmicro1065

Dosage Regimens of Antibacterials: Implications of a Pharmacokinetic/Pharmacodynamic Model
journal, January 1996

A simple in vitro PK/PD model system to determine time–kill curves of drugs against Mycobacteria
journal, September 2009

Why Is Long-Term Therapy Required to Cure Tuberculosis?
journal, March 2007

Time-kill kinetics of anti-tuberculosis drugs, and emergence of resistance, in relation to metabolic activity of Mycobacterium tuberculosis
journal, October 2010

  • de Steenwinkel, J. E. M.; de Knegt, G. J.; ten Kate, M. T.
  • Journal of Antimicrobial Chemotherapy, Vol. 65, Issue 12
  • DOI: 10.1093/jac/dkq374

Drug Forgiveness and Interpatient Pharmacokinetic Variability in Tuberculosis
journal, October 2011

The path of anti-tuberculosis drugs: from blood to lesions to mycobacterial cells
journal, February 2014

Population Pharmacokinetics and its Role in Anti-Tuberculosis Drug Development and Optimization of Treatment
journal, September 2011

  • Free Egelund, Eric; Bergesch Barth, Aline; Arthur Peloquin, Charles
  • Current Pharmaceutical Design, Vol. 17, Issue 27
  • DOI: 10.2174/138161211797470246

Correlation of Granuloma Structure with Clinical Outcome Defines two Types of Idiopathic Disseminated bcg Infection
journal, January 1997

Multiscale Computational Modeling Reveals a Critical Role for TNF-α Receptor 1 Dynamics in Tuberculosis Granuloma Formation
journal, February 2011

  • Fallahi-Sichani, Mohammad; El-Kebir, Mohammed; Marino, Simeone
  • The Journal of Immunology, Vol. 186, Issue 6
  • DOI: 10.4049/jimmunol.1003299

Differential Risk of Tuberculosis Reactivation among Anti-TNF Therapies Is Due to Drug Binding Kinetics and Permeability
journal, February 2012

  • Fallahi-Sichani, Mohammad; Flynn, JoAnne L.; Linderman, Jennifer J.
  • The Journal of Immunology, Vol. 188, Issue 7
  • DOI: 10.4049/jimmunol.1103298

Tumor necrosis factor-α is required in the protective immune response against mycobacterium tuberculosis in mice
journal, June 1995

Cytological and Transcript Analyses Reveal Fat and Lazy Persister-Like Bacilli in Tuberculous Sputum
journal, April 2008

Population Modeling and Monte Carlo Simulation Study of the Pharmacokinetics and Antituberculosis Pharmacodynamics of Rifampin in Lungs
journal, April 2009

  • Goutelle, S.; Bourguignon, L.; Maire, P. H.
  • Antimicrobial Agents and Chemotherapy, Vol. 53, Issue 7
  • DOI: 10.1128/AAC.01520-08

Mathematical modeling of pulmonary tuberculosis therapy: Insights from a prototype model with rifampin
journal, August 2011

  • Goutelle, Sylvain; Bourguignon, Laurent; Jelliffe, Roger W.
  • Journal of Theoretical Biology, Vol. 282, Issue 1
  • DOI: 10.1016/j.jtbi.2011.05.013

Nanobead-based interventions for the treatment and prevention of tuberculosis
journal, October 2010

  • Griffiths, Gareth; Nyström, Bo; Sable, Suraj B.
  • Nature Reviews Microbiology, Vol. 8, Issue 11
  • DOI: 10.1038/nrmicro2437

On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient
journal, June 2012

  • Hermsen, R.; Deris, J. B.; Hwa, T.
  • Proceedings of the National Academy of Sciences, Vol. 109, Issue 27
  • DOI: 10.1073/pnas.1117716109

Tuberculosis therapy: past, present and future
journal, July 2002

In Silico Children and the Glass Mouse Model: Clinical Trial Simulations To Identify and Individualize Optimal Isoniazid Doses in Children with Tuberculosis
journal, November 2010

  • Jeena, Prakash M.; Bishai, William R.; Pasipanodya, Jotam G.
  • Antimicrobial Agents and Chemotherapy, Vol. 55, Issue 2
  • DOI: 10.1128/AAC.00763-10

Bactericidal and Sterilizing Activities of Antituberculosis Drugs during the First 14 Days
journal, May 2003

  • Jindani, Amina; Doré, Caroline J.; Mitchison, Denis A.
  • American Journal of Respiratory and Critical Care Medicine, Vol. 167, Issue 10
  • DOI: 10.1164/rccm.200210-1125OC

Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models: Tuneable resolution as a systems biology approach
journal, July 2014

  • Kirschner, Denise E.; Hunt, C. Anthony; Marino, Simeone
  • Wiley Interdisciplinary Reviews: Systems Biology and Medicine, Vol. 6, Issue 4
  • DOI: 10.1002/wsbm.1270

Pharmacokinetic Evaluation of the Penetration of Antituberculosis Agents in Rabbit Pulmonary Lesions
journal, October 2011

  • Kjellsson, Maria C.; Via, Laura E.; Goh, Anne
  • Antimicrobial Agents and Chemotherapy, Vol. 56, Issue 1
  • DOI: 10.1128/AAC.05208-11

A model of isoniazid treatment of tuberculosis
journal, December 2014

Early Events in Mycobacterium tuberculosis Infection in Cynomolgus Macaques
journal, June 2006

  • Lin, P. L.; Pawar, S.; Myers, A.
  • Infection and Immunity, Vol. 74, Issue 7
  • DOI: 10.1128/IAI.00064-06

Quantitative Comparison of Active and Latent Tuberculosis in the Cynomolgus Macaque Model
journal, July 2009

  • Lin, P. L.; Rodgers, M.; Smith, L.
  • Infection and Immunity, Vol. 77, Issue 10
  • DOI: 10.1128/IAI.00592-09

Metronidazole prevents reactivation of latent Mycobacterium tuberculosis infection in macaques
journal, July 2012

  • Lin, P. L.; Dartois, V.; Johnston, P. J.
  • Proceedings of the National Academy of Sciences, Vol. 109, Issue 35
  • DOI: 10.1073/pnas.1121497109

Radiologic Responses in Cynomolgus Macaques for Assessing Tuberculosis Chemotherapy Regimens
journal, June 2013

  • Lin, Philana Ling; Coleman, Teresa; Carney, Jonathan P. J.
  • Antimicrobial Agents and Chemotherapy, Vol. 57, Issue 9
  • DOI: 10.1128/AAC.00277-13

Sterilization of granulomas is common in active and latent tuberculosis despite within-host variability in bacterial killing
journal, December 2013

  • Lin, Philana Ling; Ford, Christopher B.; Coleman, M. Teresa
  • Nature Medicine, Vol. 20, Issue 1
  • DOI: 10.1038/nm.3412

Pharmacokinetics of oral and intravenous rifampicin during chronic administration
journal, December 1985

  • Loos, U.; Musch, E.; Jensen, J. C.
  • Klinische Wochenschrift, Vol. 63, Issue 23
  • DOI: 10.1007/BF01733779

A Physiologically Based Pharmacokinetic Model of Rifampin in Mice
journal, January 2013

  • Lyons, Michael A.; Reisfeld, Brad; Yang, Raymond S. H.
  • Antimicrobial Agents and Chemotherapy, Vol. 57, Issue 4
  • DOI: 10.1128/AAC.01567-12

A Novel Metabolite of Antituberculosis Therapy Demonstrates Host Activation of Isoniazid and Formation of the Isoniazid-NAD + Adduct
journal, October 2011

  • Mahapatra, Sebabrata; Woolhiser, Lisa K.; Lenaerts, Anne J.
  • Antimicrobial Agents and Chemotherapy, Vol. 56, Issue 1
  • DOI: 10.1128/AAC.05486-11

Differences in Reactivation of Tuberculosis Induced from Anti-TNF Treatments Are Based on Bioavailability in Granulomatous Tissue
journal, January 2007

A methodology for performing global uncertainty and sensitivity analysis in systems biology
journal, September 2008

  • Marino, Simeone; Hogue, Ian B.; Ray, Christian J.
  • Journal of Theoretical Biology, Vol. 254, Issue 1
  • DOI: 10.1016/j.jtbi.2008.04.011

Update—Pathogens of concern
journal, December 2013

Comparison of activities of rifapentine and rifampin against Mycobacterium tuberculosis residing in human macrophages
journal, September 1995

  • Mor, N.; Simon, B.; Mezo, N.
  • Antimicrobial Agents and Chemotherapy, Vol. 39, Issue 9
  • DOI: 10.1128/AAC.39.9.2073

Fast fourier transform method for partial differential equations, case study: The 2-D diffusion equation
journal, January 1988

Population pharmacokinetic modeling of isoniazid, rifampin, and pyrazinamide
journal, December 1997

  • Peloquin, C. A.; Jaresko, G. S.; Yong, C. L.
  • Antimicrobial Agents and Chemotherapy, Vol. 41, Issue 12
  • DOI: 10.1128/AAC.41.12.2670

Activities of Drug Combinations against Mycobacterium tuberculosis Grown in Aerobic and Hypoxic Acidic Conditions
journal, January 2013

  • Piccaro, Giovanni; Giannoni, Federico; Filippini, Perla
  • Antimicrobial Agents and Chemotherapy, Vol. 57, Issue 3
  • DOI: 10.1128/AAC.02154-12

High-Sensitivity MALDI-MRM-MS Imaging of Moxifloxacin Distribution in Tuberculosis-Infected Rabbit Lungs and Granulomatous Lesions
journal, March 2011

  • Prideaux, Brendan; Dartois, Véronique; Staab, Dieter
  • Analytical Chemistry, Vol. 83, Issue 6
  • DOI: 10.1021/ac1029049

Prediction of Tumour Tissue Diffusion Coefficients of Hypoxia-Activated Prodrugs from Physicochemical Parameters
journal, January 2008

  • Pruijn, Frederik B.; Patel, Kashyap; Hay, Michael P.
  • Australian Journal of Chemistry, Vol. 61, Issue 9
  • DOI: 10.1071/CH08240

Synergy between Individual TNF-Dependent Functions Determines Granuloma Performance for Controlling Mycobacterium tuberculosis Infection
journal, March 2009

  • Ray, J. Christian J.; Flynn, JoAnne L.; Kirschner, Denise E.
  • The Journal of Immunology, Vol. 182, Issue 6
  • DOI: 10.4049/jimmunol.0802297

A Physiologically Based Pharmacokinetic Model for Capreomycin
journal, December 2011

  • Reisfeld, B.; Metzler, C. P.; Lyons, M. A.
  • Antimicrobial Agents and Chemotherapy, Vol. 56, Issue 2
  • DOI: 10.1128/AAC.05180-11

Experimental epithelioid cell granulomas tubercle formation and immunological competence: An ultrastructural analysis
journal, October 1983

  • Ridley, Marian J.; Heather, C. J.; Brown, I.
  • The Journal of Pathology, Vol. 141, Issue 2
  • DOI: 10.1002/path.1711410202

Cytokines as Immunomodulators in Tuberculosis Therapy
journal, November 2008

Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model
journal, December 2004

  • Segovia-Juarez, Jose L.; Ganguli, Suman; Kirschner, Denise
  • Journal of Theoretical Biology, Vol. 231, Issue 3
  • DOI: 10.1016/j.jtbi.2004.06.031

Moxifloxacin, Ofloxacin, Sparfloxacin, and Ciprofloxacin against Mycobacterium tuberculosis : Evaluation of In Vitro and Pharmacodynamic Indices That Best Predict In Vivo Efficacy
journal, February 2007

  • Shandil, Radha K.; Jayaram, Ramesh; Kaur, Parvinder
  • Antimicrobial Agents and Chemotherapy, Vol. 51, Issue 2
  • DOI: 10.1128/AAC.00414-06

Multidrug-Resistant Tuberculosis Not Due to Noncompliance but to Between-Patient Pharmacokinetic Variability
journal, October 2011

  • Srivastava, Shashikant; Pasipanodya, Jotam G.; Meek, Claudia
  • The Journal of Infectious Diseases, Vol. 204, Issue 12
  • DOI: 10.1093/infdis/jir658

Adjunct Immunotherapies for Tuberculosis
journal, March 2012

  • Uhlin, M.; Andersson, J.; Zumla, A.
  • Journal of Infectious Diseases, Vol. 205, Issue suppl 2
  • DOI: 10.1093/infdis/jis197

Month 2 Culture Status and Treatment Duration as Predictors of Tuberculosis Relapse Risk in a Meta-Regression Model
journal, August 2013

Population Pharmacokinetics of Rifampin in Pulmonary Tuberculosis Patients, Including a Semimechanistic Model To Describe Variable Absorption
journal, April 2008

  • Wilkins, Justin J.; Savic, Radojka M.; Karlsson, Mats O.
  • Antimicrobial Agents and Chemotherapy, Vol. 52, Issue 6
  • DOI: 10.1128/AAC.00461-07

Variability in the population pharmacokinetics of isoniazid in South African tuberculosis patients: Population pharmacokinetics of isoniazid
journal, June 2011

Acceleration of Emergence of Bacterial Antibiotic Resistance in Connected Microenvironments
journal, September 2011

Advances in the development of new tuberculosis drugs and treatment regimens
journal, April 2013

  • Zumla, Alimuddin; Nahid, Payam; Cole, Stewart T.
  • Nature Reviews Drug Discovery, Vol. 12, Issue 5
  • DOI: 10.1038/nrd4001
    Sort by:
    [ × clear filter / sort ]

    Works referencing / citing this record:

    A computational model of invasive aspergillosis in the lung and the role of iron
    journal, April 2016

    • Oremland, Matthew; Michels, Kathryn R.; Bettina, Alexandra M.
    • BMC Systems Biology, Vol. 10, Issue 1
    • DOI: 10.1186/s12918-016-0275-2

    Integrating Antimicrobial Therapy with Host Immunity to Fight Drug-Resistant Infections: Classical vs. Adaptive Treatment
    journal, April 2016

    Host-directed therapy targeting the Mycobacterium tuberculosis granuloma: a review
    journal, October 2015

    • Kiran, Dilara; Podell, Brendan K.; Chambers, Mark
    • Seminars in Immunopathology, Vol. 38, Issue 2
    • DOI: 10.1007/s00281-015-0537-x

    A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection
    journal, October 2016

    Gene expression models based on a reference laboratory strain are poor predictors of Mycobacterium tuberculosis complex transcriptional diversity
    journal, February 2018

    • Chiner-Oms, Álvaro; González-Candelas, Fernando; Comas, Iñaki
    • Scientific Reports, Vol. 8, Issue 1
    • DOI: 10.1038/s41598-018-22237-5

    Fluoroquinolone Efficacy against Tuberculosis Is Driven by Penetration into Lesions and Activity against Resident Bacterial Populations
    journal, February 2019

    • Sarathy, Jansy; Blanc, Landry; Alvarez-Cabrera, Nadine
    • Antimicrobial Agents and Chemotherapy, Vol. 63, Issue 5
    • DOI: 10.1128/aac.02516-18

    Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens
    journal, August 2017

    • Cicchese, Joseph M.; Pienaar, Elsje; Kirschner, Denise E.
    • Cellular and Molecular Bioengineering, Vol. 10, Issue 6
    • DOI: 10.1007/s12195-017-0507-6

    Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems
    journal, January 2018

    Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas
    journal, May 2018

    In silico evaluation and exploration of antibiotic tuberculosis treatment regimens
    journal, November 2015

    • Pienaar, Elsje; Dartois, Véronique; Linderman, Jennifer J.
    • BMC Systems Biology, Vol. 9, Issue 1
    • DOI: 10.1186/s12918-015-0221-8

    Modeling Granulomas in Response to Infection in the Lung
    journal, March 2016

    Low Levels of T Cell Exhaustion in Tuberculous Lung Granulomas
    journal, June 2018

    • Wong, Eileen A.; Joslyn, Louis; Grant, Nicole L.
    • Infection and Immunity, Vol. 86, Issue 9
    • DOI: 10.1128/iai.00426-18

    Spatiotemporal Dynamics of Virus Infection Spreading in Tissues
    journal, December 2016

    Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology
    journal, August 2018

    • Cicchese, Joseph M.; Evans, Stephanie; Hult, Caitlin
    • Immunological Reviews, Vol. 285, Issue 1
    • DOI: 10.1111/imr.12671

    Agent‐based models of inflammation in translational systems biology: A decade later
    journal, June 2019

    • Vodovotz, Yoram; An, Gary
    • Wiley Interdisciplinary Reviews: Systems Biology and Medicine, Vol. 11, Issue 6
    • DOI: 10.1002/wsbm.1460

    A multi-scale approach to designing therapeutics for tuberculosis
    journal, April 2015

    • Linderman, Jennifer J.; Cilfone, Nicholas A.; Pienaar, Elsje
    • Integrative Biology, Vol. 7, Issue 5
    • DOI: 10.1039/c4ib00295d

    Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach
    journal, August 2017

    Agent‐based models of inflammation in translational systems biology: A decade later
    journal, June 2019

    • Vodovotz, Yoram; An, Gary
    • Wiley Interdisciplinary Reviews: Systems Biology and Medicine, Vol. 11, Issue 6
    • DOI: 10.1002/wsbm.1460

    Host-directed therapy targeting the Mycobacterium tuberculosis granuloma: a review
    journal, October 2015

    • Kiran, Dilara; Podell, Brendan K.; Chambers, Mark
    • Seminars in Immunopathology, Vol. 38, Issue 2
    • DOI: 10.1007/s00281-015-0537-x

    Gene expression models based on a reference laboratory strain are poor predictors of Mycobacterium tuberculosis complex transcriptional diversity
    journal, February 2018

    • Chiner-Oms, Álvaro; González-Candelas, Fernando; Comas, Iñaki
    • Scientific Reports, Vol. 8, Issue 1
    • DOI: 10.1038/s41598-018-22237-5

    Facing the challenges of multiscale modelling of bacterial and fungal pathogen–host interactions
    journal, February 2016

    • Schleicher, Jana; Conrad, Theresia; Gustafsson, Mika
    • Briefings in Functional Genomics
    • DOI: 10.1093/bfgp/elv064

    Fluoroquinolone Efficacy against Tuberculosis Is Driven by Penetration into Lesions and Activity against Resident Bacterial Populations
    journal, February 2019

    • Sarathy, Jansy; Blanc, Landry; Alvarez-Cabrera, Nadine
    • Antimicrobial Agents and Chemotherapy, Vol. 63, Issue 5
    • DOI: 10.1128/aac.02516-18

    In silico evaluation and exploration of antibiotic tuberculosis treatment regimens
    journal, November 2015

    • Pienaar, Elsje; Dartois, Véronique; Linderman, Jennifer J.
    • BMC Systems Biology, Vol. 9, Issue 1
    • DOI: 10.1186/s12918-015-0221-8

    A computational model of invasive aspergillosis in the lung and the role of iron
    journal, April 2016

    • Oremland, Matthew; Michels, Kathryn R.; Bettina, Alexandra M.
    • BMC Systems Biology, Vol. 10, Issue 1
    • DOI: 10.1186/s12918-016-0275-2

    Integrating Antimicrobial Therapy with Host Immunity to Fight Drug-Resistant Infections: Classical vs. Adaptive Treatment
    journal, April 2016

    Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies
    journal, January 2017

    Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach
    journal, August 2017

    Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation
    journal, December 2020

    Modeling Granulomas in Response to Infection in the Lung
    journal, March 2016

    Spatiotemporal Dynamics of Virus Infection Spreading in Tissues
    journal, December 2016

    Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas
    journal, May 2018

    Strategic Priming with Multiple Antigens can Yield Memory Cell Phenotypes Optimized for Infection with Mycobacterium tuberculosis: A Computational Study
    journal, January 2016

    Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas
    journal, March 2020

    • Cicchese, Joseph M.; Dartois, Véronique; Kirschner, Denise E.
    • Frontiers in Pharmacology, Vol. 11
    • DOI: 10.3389/fphar.2020.00333

    A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection
    journal, October 2016

      Sort by:
      [ × clear filter / sort ]

      Figures / Tables found in this record:

      Figure 1 (p. 24)figure
      Figure 2 (p. 25)figure
      Figure 3 (p. 26)figure
      Figure 4 (p. 27)figure
      Figure 5 (p. 28)figure
      Figure 6 (p. 29)figure
      Table 1 (p. 30)table
        [ × clear filter / sort ]
        Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.

        Similar Records in DOE PAGES and OSTI.GOV collections: