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PUBMED FOR HANDHELDS

Journal Abstract Search


183 related items for PubMed ID: 34343169

  • 1. A multi-approach and multi-scale platform to model CD4+ T cells responding to infections.
    Wertheim KY, Puniya BL, La Fleur A, Shah AR, Barberis M, Helikar T.
    PLoS Comput Biol; 2021 Aug; 17(8):e1009209. PubMed ID: 34343169
    [Abstract] [Full Text] [Related]

  • 2. Hybrid approach to model the spatial regulation of T cell responses.
    Bouchnita A, Bocharov G, Meyerhans A, Volpert V.
    BMC Immunol; 2017 Jun 21; 18(Suppl 1):29. PubMed ID: 28681703
    [Abstract] [Full Text] [Related]

  • 3. Predicting lymph node output efficiency using systems biology.
    Gong C, Mattila JT, Miller M, Flynn JL, Linderman JJ, Kirschner D.
    J Theor Biol; 2013 Oct 21; 335():169-84. PubMed ID: 23816876
    [Abstract] [Full Text] [Related]

  • 4. Characterizing the dynamics of CD4+ T cell priming within a lymph node.
    Linderman JJ, Riggs T, Pande M, Miller M, Marino S, Kirschner DE.
    J Immunol; 2010 Mar 15; 184(6):2873-85. PubMed ID: 20154206
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  • 5. Predictive computational modeling of the mucosal immune responses during Helicobacter pylori infection.
    Carbo A, Bassaganya-Riera J, Pedragosa M, Viladomiu M, Marathe M, Eubank S, Wendelsdorf K, Bisset K, Hoops S, Deng X, Alam M, Kronsteiner B, Mei Y, Hontecillas R.
    PLoS One; 2013 Mar 15; 8(9):e73365. PubMed ID: 24039925
    [Abstract] [Full Text] [Related]

  • 6. Optimisation and parallelisation strategies for Monte Carlo simulation of HIV infection.
    Hecquet D, Ruskin HJ, Crane M.
    Comput Biol Med; 2007 May 15; 37(5):691-9. PubMed ID: 16901479
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  • 7. Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome.
    Marino S, Gideon HP, Gong C, Mankad S, McCrone JT, Lin PL, Linderman JJ, Flynn JL, Kirschner DE.
    PLoS Comput Biol; 2016 Apr 15; 12(4):e1004804. PubMed ID: 27065304
    [Abstract] [Full Text] [Related]

  • 8. TCM-1: a nonlinear dynamical computational model to simulate cellular changes in the T cell system; conceptional design and validation.
    Krueger GR, Brandt ME, Wang G, Buja LM.
    Anticancer Res; 2003 Apr 15; 23(1A):123-35. PubMed ID: 12680203
    [Abstract] [Full Text] [Related]

  • 9. Multiscale modeling of mucosal immune responses.
    Mei Y, Abedi V, Carbo A, Zhang X, Lu P, Philipson C, Hontecillas R, Hoops S, Liles N, Bassaganya-Riera J.
    BMC Bioinformatics; 2015 Apr 15; 16 Suppl 12(Suppl 12):S2. PubMed ID: 26329787
    [Abstract] [Full Text] [Related]

  • 10. Stochastic modeling of the dynamics of CD4+ T-cell infection by HIV and some Monte Carlo studies.
    Tan WY, Wu H.
    Math Biosci; 1998 Jan 15; 147(2):173-205. PubMed ID: 9433062
    [Abstract] [Full Text] [Related]

  • 11. Systems biology in immunology: a computational modeling perspective.
    Germain RN, Meier-Schellersheim M, Nita-Lazar A, Fraser ID.
    Annu Rev Immunol; 2011 Jan 15; 29():527-85. PubMed ID: 21219182
    [Abstract] [Full Text] [Related]

  • 12. A mathematical model of HIV dynamics treated with a population of gene-edited haematopoietic progenitor cells exhibiting threshold phenomenon.
    Ratti V, Nanda S, Eszterhas SK, Howell AL, Wallace DI.
    Math Med Biol; 2020 May 29; 37(2):212-242. PubMed ID: 31265056
    [Abstract] [Full Text] [Related]

  • 13. Modeling the Slow CD4+ T Cell Decline in HIV-Infected Individuals.
    Wang S, Hottz P, Schechter M, Rong L.
    PLoS Comput Biol; 2015 Dec 29; 11(12):e1004665. PubMed ID: 26709961
    [Abstract] [Full Text] [Related]

  • 14. A Mathematical Framework for Understanding Four-Dimensional Heterogeneous Differentiation of CD4+ T Cells.
    Hong T, Oguz C, Tyson JJ.
    Bull Math Biol; 2015 Jun 29; 77(6):1046-64. PubMed ID: 25779890
    [Abstract] [Full Text] [Related]

  • 15. Systems modeling of molecular mechanisms controlling cytokine-driven CD4+ T cell differentiation and phenotype plasticity.
    Carbo A, Hontecillas R, Kronsteiner B, Viladomiu M, Pedragosa M, Lu P, Philipson CW, Hoops S, Marathe M, Eubank S, Bisset K, Wendelsdorf K, Jarrah A, Mei Y, Bassaganya-Riera J.
    PLoS Comput Biol; 2013 Apr 29; 9(4):e1003027. PubMed ID: 23592971
    [Abstract] [Full Text] [Related]

  • 16. Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).
    Foffi G, Pastore A, Piazza F, Temussi PA.
    Phys Biol; 2013 Aug 29; 10(4):040301. PubMed ID: 23912807
    [Abstract] [Full Text] [Related]

  • 17. Stochastic cellular automata model and Monte Carlo simulations of CD4+ T cell dynamics with a proposed alternative leukapheresis treatment for HIV/AIDS.
    Precharattana M, Nokkeaw A, Triampo W, Triampo D, Lenbury Y.
    Comput Biol Med; 2011 Jul 29; 41(7):546-58. PubMed ID: 21621202
    [Abstract] [Full Text] [Related]

  • 18. The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models.
    Palsson S, Hickling TP, Bradshaw-Pierce EL, Zager M, Jooss K, O'Brien PJ, Spilker ME, Palsson BO, Vicini P.
    BMC Syst Biol; 2013 Sep 28; 7():95. PubMed ID: 24074340
    [Abstract] [Full Text] [Related]

  • 19. The in vitro MIMIC® platform reflects age-associated changes in immunological responses after influenza vaccination.
    Dauner A, Agrawal P, Salvatico J, Tapia T, Dhir V, Shaik SF, Drake DR, Byers AM.
    Vaccine; 2017 Oct 04; 35(41):5487-5494. PubMed ID: 28413134
    [Abstract] [Full Text] [Related]

  • 20. CD4+ T-cell memory: generation and multi-faceted roles for CD4+ T cells in protective immunity to influenza.
    Swain SL, Agrewala JN, Brown DM, Jelley-Gibbs DM, Golech S, Huston G, Jones SC, Kamperschroer C, Lee WH, McKinstry KK, Román E, Strutt T, Weng NP.
    Immunol Rev; 2006 Jun 04; 211():8-22. PubMed ID: 16824113
    [Abstract] [Full Text] [Related]


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