Dataset Information

Nelson2000_HIV-1_intracellular_delay

ABSTRACT: described in: A model of HIV-1 pathogenesis that includes an intracellular delay. Nelson PW, Murray JD, Perelson AS; Math Biosci. 2000 Feb;163(2):201-15. PMID: 10701304 ; doi: 10.1016/S0025-5564(99)00055-3 Abstract: Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection. Author Keywords: HIV; Delay; Viral life cycle; T-cells As the initial conditions for the intergrations where not known, approximate inital conditions for the steady state at k=3.47*10 -8 where chosen ( T * =1675 ). The model does not reprodue the results given in the article quantitatively, but the time courses are quite similar. For running the model, the initial conditions for t should be the same as the steady state ones. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

DISEASE(S): Human Immunodeficiency Virus Infectious Disease

SUBMITTER: Lukas Endler

PROVIDER: MODEL8102792069 | BioModels | 2005-01-01

REPOSITORIES: BioModels

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A model of HIV-1 pathogenesis that includes an intracellular delay.

Nelson P W PW Murray J D JD Perelson A S AS

Mathematical biosciences 20000201 2

Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We cons ...[more]

PMID: 10701304

Similar Datasets

Project description:This is the general model without delay described by the equation system (1) in: A model of HIV-1 pathogenesis that includes an intracellular delay. Nelson PW, Murray JD, Perelson AS; Math Biosci. 2000 Feb;163(2):201-15. PMID: 10701304 ; doi: 10.1016/S0025-5564(99)00055-3 Abstract: Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection. Author Keywords: HIV; Delay; Viral life cycle; T-cells As there are no results given for this model in the article it cannot be checked for MIRIAM compliance. The SBML file should be equivalent to the described ODE file though. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Project description:Wodarz2007 - HIV/CD4 T-cell interaction A deterministic model illustrating how CD4 T-cells can influence HIV infection. This model is described in the article: Infection dynamics in HIV-specific CD4 T cells: does a CD4 T cell boost benefit the host or the virus? Wodarz D, Hamer DH. Math Biosci 2007 Sep; 209(1): 14-29 Abstract: Recent experimental data have shown that HIV-specific CD4 T cells provide a very important target for HIV replication. We use mathematical models to explore the effect of specific CD4 T cell infection on the dynamics of virus spread and immune responses. Infected CD4 T cells can provide antigen for their own stimulation. We show that such autocatalytic cell division can significantly enhance virus spread, and can also provide an additional reservoir for virus persistence during anti-viral drug therapy. In addition, the initial number of HIV-specific CD4 T cells is an important determinant of acute infection dynamics. A high initial number of HIV-specific CD4 T cells can lead to a sudden and fast drop of the population of HIV-specific CD4 T cells which results quickly in their extinction. On the other hand, a low initial number of HIV-specific CD4 T cells can lead to a prolonged persistence of HIV-specific CD4 T cell help at higher levels. The model suggests that boosting the population of HIV-specific CD4 T cells can increase the amount of virus-induced immune impairment, lead to less efficient anti-viral effector responses, and thus speed up disease progression, especially if effector responses such as CTL have not been sufficiently boosted at the same time. This model is hosted on BioModels Database and identified by: BIOMD0000000663. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:This a model from the article: Influence of delayed viral production on viral dynamics in HIV-1 infected patients. Mittler JE, Sulzer B, Neumann AU, Perelson AS. Math Biosci 1998 Sep;152(2):143-63 9780612 , Abstract: We present and analyze a model for the interaction of human immunodeficiency virus type 1 (HIV-1) with target cells that includes a time delay between initial infection and the formation of productively infected cells. Assuming that the variation among cells with respect to this 'intracellular' delay can be approximated by a gamma distribution, a high flexible distribution that can mimic a variety of biologically plausible delays, we provide analytical solutions for the expected decline in plasma virus concentration after the initiation of antiretroviral therapy with one or more protease inhibitors. We then use the model to investigate whether the parameters that characterize viral dynamics can be identified from biological data. Using non-linear least-squares regression to fit the model to simulated data in which the delays conform to a gamma distribution, we show that good estimates for free viral clearance rates, infected cell death rates, and parameters characterizing the gamma distribution can be obtained. For simulated data sets in which the delays were generated using other biologically plausible distributions, reasonably good estimates for viral clearance rates, infected cell death rates, and mean delay times can be obtained using the gamma-delay model. For simulated data sets that include added simulated noise, viral clearance rate estimates are not as reliable. If the mean intracellular delay is known, however, we show that reasonable estimates for the viral clearance rate can be obtained by taking the harmonic mean of viral clearance rate estimates from a group of patients. These results demonstrate that it is possible to incorporate distributed intracellular delays into existing models for HIV dynamics and to use these refined models to estimate the half-life of free virus from data on the decline in HIV-1 RNA following treatment. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Mittler JE, Sulzer B, Neumann AU, Perelson AS. (1998) - version=1.0 The original CellML model was created by: Catherine Lloyd c.lloyd@auckland.ac.nz The University of Auckland This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Project description:Macrophages provide an interface between innate and adaptive immunity and are important long-lived reservoirs for Human Immunodeficiency Virus Type-1 (HIV-1). Multiple genetic networks involved in regulating signal transduction cascades and immune responses in macrophages are coordinately modulated by HIV-1 infection. To evaluate complex interrelated processes and to assemble an integrated view of activated signaling networks, a systems biology strategy was applied to genomic and proteomic responses by primary human macrophages over the course of HIV-1 infection. Macrophage responses, including cell cycle, calcium, apoptosis, mitogen-activated protein kinases (MAPK), and cytokines/chemokines, to HIV-1 were temporally regulated, in the absence of cell proliferation. In contrast, Toll-like receptor (TLR) pathways remained unaltered by HIV-1, although TLRs 3, 4, 7, and 8 were expressed and responded to ligand stimulation in macrophages. HIV-1 failed to activate phosphorylation of IRAK-1 or IRF-3, modulate intracellular protein levels of Mx1, an interferon-stimulated gene, or stimulate secretion of TNF, IL-1b, or IL-6. Activation of pathways other than TLR was inadequate to stimulate, via cross-talk mechanisms through molecular hubs, the production of proinflammatory cytokines typical of a TLR response. HIV-1 sensitized macrophage responses to TLR ligands, and the magnitude of viral priming was related to virus replication. HIV-1 induced a primed, proinflammatory state, M1HIV, which increased the responsiveness of macrophages to TLR ligands. HIV-1 might passively evade pattern recognition, actively inhibit or suppress recognition and signaling, or require dynamic interactions between macrophages and other cells, such as lymphocytes or endothelial cells. HIV-1 evasion of TLR recognition and simultaneous priming of macrophages may represent a strategy for viral survival, contribute to immune pathogenesis, and provide important targets for therapeutic approaches. Affymetrix arrays were used to identify genomic macrophage response to HIV during viral spread in culture. Experiment Overall Design: An HIV-1 spreading infection was established in primary human macrophages. RNA was extracted from both viral- and mock-infected macrophages cultures over 7 days and hybridized to Affymetrix HG-U95Av2 GeneChips for analysis.

Project description:This a model from the article: Fighting a virus with a virus: a dynamic model for HIV-1 therapy. Revilla T, Garcia-Ramos G. Math Biosci 2003 Oct;185(2):191-203 12941536 , Abstract: A mathematical model examined a potential therapy for controlling viral infections using genetically modified viruses. The control of the infection is an indirect effect of the selective elimination by an engineered virus of infected cells that are the source of the pathogens. Therefore, this engineered virus could greatly compensate for a dysfunctional immune system compromised by AIDS. In vitro studies using engineered viruses have been shown to decrease the HIV-1 load about 1000-fold. However, the efficacy of this potential treatment for reducing the viral load in AIDS patients is unknown. The present model studied the interactions among the HIV-1 virus, its main host cell (activated CD4+ T cells), and a therapeutic engineered virus in an in vivo context; and it examined the conditions for controlling the pathogen. This model predicted a significant drop in the HIV-1 load, but the treatment does not eradicate HIV. A basic estimation using a currently engineered virus indicated an HIV-1 load reduction of 92% and a recovery of host cells to 17% of their normal level. Greater success (98% HIV reduction, 44% host cells recovery) is expected as more competent engineered viruses are designed. These results suggest that therapy using viruses could be an alternative to extend the survival of AIDS patients. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Revilla T, Garcia-Ramos G. (2003) - version=1.0 The original CellML model was created by: Catherine Lloyd c.lloyd@auckland.ac.nz The University of Auckland This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.