Nelson1995_HIV1therapy_DIVadministraion_ModelA
Ontology highlight
ABSTRACT:
SUBMITTER: 
Camille Laibe
PROVIDER: MODEL1006230081 | BioModels | 2005-01-01
REPOSITORIES: BioModels
Dataset's files
Items per page: 5 1 - 5 of 11 |
Publications
Modeling defective interfering virus therapy for AIDS: conditions for DIV survival.
Mathematical biosciences 19950201 2
The administration of a genetically engineered defective interfering virus (DIV) that interferes with HIV-1 replication has been proposed as a therapy for HIV-1 infection and AIDS. The proposed interfering virus, which is designed to superinfect HIV-1 infected cells, carries ribozymes that cleave conserved regions in HIV-1 RNA that code for the viral envelope protein. Thus DIV infection of HIV-1 infected cells should reduce or eliminate viral production by these cells. The success of this therap ...[more]
Similar Datasets
Project description:This a model from the article:
Modeling defective interfering virus therapy for AIDS: conditions for DIV
survival.
Nelson GW, Perelson AS. Math Biosci
1995 Feb;125(2):127-53 7881191
,
Abstract:
The administration of a genetically engineered defective interfering virus (DIV)
that interferes with HIV-1 replication has been proposed as a therapy for HIV-1
infection and AIDS. The proposed interfering virus, which is designed to
superinfect HIV-1 infected cells, carries ribozymes that cleave conserved
regions in HIV-1 RNA that code for the viral envelope protein. Thus DIV
infection of HIV-1 infected cells should reduce or eliminate viral production by
these cells. The success of this therapeutic strategy will depend both on the
intercellular interaction of DIV and HIV-1, and on the overall dynamics of virus
and T cells in the body. To study these dynamical issues, we have constructed a
mathematical model of the interaction of HIV-1, DIV, and CD4+ cells in vivo. The
results of both mathematical analysis and numerical simulation indicate that
survival of the engineered DIV purely on a peripheral blood HIV-1 infection is
unlikely. However, analytical results indicate that DIV might well survive on
HIV-1 infected CD4+ cells in lymphoid organs such as lymph nodes and spleen, or
on other HIV-1 infected cells in these organs.
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
Nelson GW, Perelson AS. (1995) - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.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.
2005-01-01 | MODEL1006230033 | BioModels
Project description: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.
2005-01-01 | MODEL8102792069 | BioModels
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.
2024-09-02 | BIOMD0000000875 | BioModels
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.
2024-09-02 | BIOMD0000000707 | BioModels
Project description:This a model from the article:
Dynamics of HIV infection of CD4+ T cells.
Perelson AS, Kirschner DE, De Boer R. Math Biosci
1993 Mar;114(1):81-125 8096155
,
Abstract:
We examine a model for the interaction of HIV with CD4+ T cells that considers
four populations: uninfected T cells, latently infected T cells, actively
infected T cells, and free virus. Using this model we show that many of the
puzzling quantitative features of HIV infection can be explained simply. We also
consider effects of AZT on viral growth and T-cell population dynamics. The
model exhibits two steady states, an uninfected state in which no virus is
present and an endemically infected state, in which virus and infected T cells
are present. We show that if N, the number of infectious virions produced per
actively infected T cell, is less a critical value, Ncrit, then the uninfected
state is the only steady state in the nonnegative orthant, and this state is
stable. For N > Ncrit, the uninfected state is unstable, and the endemically
infected state can be either stable, or unstable and surrounded by a stable
limit cycle. Using numerical bifurcation techniques we map out the parameter
regimes of these various behaviors. oscillatory behavior seems to lie outside
the region of biologically realistic parameter values. When the endemically
infected state is stable, it is characterized by a reduced number of T cells
compared with the uninfected state. Thus T-cell depletion occurs through the
establishment of a new steady state. The dynamics of the establishment of this
new steady state are examined both numerically and via the quasi-steady-state
approximation. We develop approximations for the dynamics at early times in
which the free virus rapidly binds to T cells, during an intermediate time scale
in which the virus grows exponentially, and a third time scale on which viral
growth slows and the endemically infected steady state is approached. Using the
quasi-steady-state approximation the model can be simplified to two ordinary
differential equations the summarize much of the dynamical behavior. We compute
the level of T cells in the endemically infected state and show how that level
varies with the parameters in the model. The model predicts that different viral
strains, characterized by generating differing numbers of infective virions
within infected T cells, can cause different amounts of T-cell depletion and
generate depletion at different rates. Two versions of the model are studied. In
one the source of T cells from precursors is constant, whereas in the other the
source of T cells decreases with viral load, mimicking the infection and killing
of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS)
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
Perelson AS, Kirschner DE, De Boer R. (1993) - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.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.
2005-01-01 | MODEL1006230035 | BioModels
Project description:This a model from the article:
Dynamics of HIV infection of CD4+ T cells.
Perelson AS, Kirschner DE, De Boer R. Math Biosci
1993 Mar;114(1):81-125 8096155
,
Abstract:
We examine a model for the interaction of HIV with CD4+ T cells that considers
four populations: uninfected T cells, latently infected T cells, actively
infected T cells, and free virus. Using this model we show that many of the
puzzling quantitative features of HIV infection can be explained simply. We also
consider effects of AZT on viral growth and T-cell population dynamics. The
model exhibits two steady states, an uninfected state in which no virus is
present and an endemically infected state, in which virus and infected T cells
are present. We show that if N, the number of infectious virions produced per
actively infected T cell, is less a critical value, Ncrit, then the uninfected
state is the only steady state in the nonnegative orthant, and this state is
stable. For N > Ncrit, the uninfected state is unstable, and the endemically
infected state can be either stable, or unstable and surrounded by a stable
limit cycle. Using numerical bifurcation techniques we map out the parameter
regimes of these various behaviors. oscillatory behavior seems to lie outside
the region of biologically realistic parameter values. When the endemically
infected state is stable, it is characterized by a reduced number of T cells
compared with the uninfected state. Thus T-cell depletion occurs through the
establishment of a new steady state. The dynamics of the establishment of this
new steady state are examined both numerically and via the quasi-steady-state
approximation. We develop approximations for the dynamics at early times in
which the free virus rapidly binds to T cells, during an intermediate time scale
in which the virus grows exponentially, and a third time scale on which viral
growth slows and the endemically infected steady state is approached. Using the
quasi-steady-state approximation the model can be simplified to two ordinary
differential equations the summarize much of the dynamical behavior. We compute
the level of T cells in the endemically infected state and show how that level
varies with the parameters in the model. The model predicts that different viral
strains, characterized by generating differing numbers of infective virions
within infected T cells, can cause different amounts of T-cell depletion and
generate depletion at different rates. Two versions of the model are studied. In
one the source of T cells from precursors is constant, whereas in the other the
source of T cells decreases with viral load, mimicking the infection and killing
of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS)
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
Perelson AS, Kirschner DE, De Boer R. (1993) - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.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.
2005-01-01 | MODEL1006230035 | BioModels
Project description:This a model from the article:
Dynamics of HIV infection of CD4+ T cells.
Perelson AS, Kirschner DE, De Boer R. Math Biosci
1993 Mar;114(1):81-125 8096155
,
Abstract:
We examine a model for the interaction of HIV with CD4+ T cells that considers
four populations: uninfected T cells, latently infected T cells, actively
infected T cells, and free virus. Using this model we show that many of the
puzzling quantitative features of HIV infection can be explained simply. We also
consider effects of AZT on viral growth and T-cell population dynamics. The
model exhibits two steady states, an uninfected state in which no virus is
present and an endemically infected state, in which virus and infected T cells
are present. We show that if N, the number of infectious virions produced per
actively infected T cell, is less a critical value, Ncrit, then the uninfected
state is the only steady state in the nonnegative orthant, and this state is
stable. For N > Ncrit, the uninfected state is unstable, and the endemically
infected state can be either stable, or unstable and surrounded by a stable
limit cycle. Using numerical bifurcation techniques we map out the parameter
regimes of these various behaviors. oscillatory behavior seems to lie outside
the region of biologically realistic parameter values. When the endemically
infected state is stable, it is characterized by a reduced number of T cells
compared with the uninfected state. Thus T-cell depletion occurs through the
establishment of a new steady state. The dynamics of the establishment of this
new steady state are examined both numerically and via the quasi-steady-state
approximation. We develop approximations for the dynamics at early times in
which the free virus rapidly binds to T cells, during an intermediate time scale
in which the virus grows exponentially, and a third time scale on which viral
growth slows and the endemically infected steady state is approached. Using the
quasi-steady-state approximation the model can be simplified to two ordinary
differential equations the summarize much of the dynamical behavior. We compute
the level of T cells in the endemically infected state and show how that level
varies with the parameters in the model. The model predicts that different viral
strains, characterized by generating differing numbers of infective virions
within infected T cells, can cause different amounts of T-cell depletion and
generate depletion at different rates. Two versions of the model are studied. In
one the source of T cells from precursors is constant, whereas in the other the
source of T cells decreases with viral load, mimicking the infection and killing
of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS)
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
Perelson AS, Kirschner DE, De Boer R. (1993) - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.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.
2005-01-01 | MODEL1006230075 | BioModels
Project description:This a model from the article:
Dynamics of HIV infection of CD4+ T cells.
Perelson AS, Kirschner DE, De Boer R. Math Biosci
1993 Mar;114(1):81-125 8096155
,
Abstract:
We examine a model for the interaction of HIV with CD4+ T cells that considers
four populations: uninfected T cells, latently infected T cells, actively
infected T cells, and free virus. Using this model we show that many of the
puzzling quantitative features of HIV infection can be explained simply. We also
consider effects of AZT on viral growth and T-cell population dynamics. The
model exhibits two steady states, an uninfected state in which no virus is
present and an endemically infected state, in which virus and infected T cells
are present. We show that if N, the number of infectious virions produced per
actively infected T cell, is less a critical value, Ncrit, then the uninfected
state is the only steady state in the nonnegative orthant, and this state is
stable. For N > Ncrit, the uninfected state is unstable, and the endemically
infected state can be either stable, or unstable and surrounded by a stable
limit cycle. Using numerical bifurcation techniques we map out the parameter
regimes of these various behaviors. oscillatory behavior seems to lie outside
the region of biologically realistic parameter values. When the endemically
infected state is stable, it is characterized by a reduced number of T cells
compared with the uninfected state. Thus T-cell depletion occurs through the
establishment of a new steady state. The dynamics of the establishment of this
new steady state are examined both numerically and via the quasi-steady-state
approximation. We develop approximations for the dynamics at early times in
which the free virus rapidly binds to T cells, during an intermediate time scale
in which the virus grows exponentially, and a third time scale on which viral
growth slows and the endemically infected steady state is approached. Using the
quasi-steady-state approximation the model can be simplified to two ordinary
differential equations the summarize much of the dynamical behavior. We compute
the level of T cells in the endemically infected state and show how that level
varies with the parameters in the model. The model predicts that different viral
strains, characterized by generating differing numbers of infective virions
within infected T cells, can cause different amounts of T-cell depletion and
generate depletion at different rates. Two versions of the model are studied. In
one the source of T cells from precursors is constant, whereas in the other the
source of T cells decreases with viral load, mimicking the infection and killing
of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS)
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
Perelson AS, Kirschner DE, De Boer R. (1993) - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.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.
2005-01-01 | MODEL1006230093 | BioModels
Project description:This a model from the article:
Dynamics of HIV infection of CD4+ T cells.
Perelson AS, Kirschner DE, De Boer R. Math Biosci
1993 Mar;114(1):81-125 8096155
,
Abstract:
We examine a model for the interaction of HIV with CD4+ T cells that considers
four populations: uninfected T cells, latently infected T cells, actively
infected T cells, and free virus. Using this model we show that many of the
puzzling quantitative features of HIV infection can be explained simply. We also
consider effects of AZT on viral growth and T-cell population dynamics. The
model exhibits two steady states, an uninfected state in which no virus is
present and an endemically infected state, in which virus and infected T cells
are present. We show that if N, the number of infectious virions produced per
actively infected T cell, is less a critical value, Ncrit, then the uninfected
state is the only steady state in the nonnegative orthant, and this state is
stable. For N > Ncrit, the uninfected state is unstable, and the endemically
infected state can be either stable, or unstable and surrounded by a stable
limit cycle. Using numerical bifurcation techniques we map out the parameter
regimes of these various behaviors. oscillatory behavior seems to lie outside
the region of biologically realistic parameter values. When the endemically
infected state is stable, it is characterized by a reduced number of T cells
compared with the uninfected state. Thus T-cell depletion occurs through the
establishment of a new steady state. The dynamics of the establishment of this
new steady state are examined both numerically and via the quasi-steady-state
approximation. We develop approximations for the dynamics at early times in
which the free virus rapidly binds to T cells, during an intermediate time scale
in which the virus grows exponentially, and a third time scale on which viral
growth slows and the endemically infected steady state is approached. Using the
quasi-steady-state approximation the model can be simplified to two ordinary
differential equations the summarize much of the dynamical behavior. We compute
the level of T cells in the endemically infected state and show how that level
varies with the parameters in the model. The model predicts that different viral
strains, characterized by generating differing numbers of infective virions
within infected T cells, can cause different amounts of T-cell depletion and
generate depletion at different rates. Two versions of the model are studied. In
one the source of T cells from precursors is constant, whereas in the other the
source of T cells decreases with viral load, mimicking the infection and killing
of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS)
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
Perelson AS, Kirschner DE, De Boer R. (1993) - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.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.
2024-09-02 | BIOMD0000000874 | BioModels
Project description:With the purpose of identifying the cascaded miRNA-mRNA regulatory relationships related to infection of HIV in gene level, we extracted mRNA from Peripheral blood mononuclear cells from 50 AIDS patients and 7 uninfected health controls. All AIDS patients in sample were received Antiretroviral Therapy. To further analysis the gene expression pattern in different virus load, we classified high virus loads (HVL, n=14) and low virus loads (LVL, n=16). 2635 and 2826 differentially expressed genes (DEGs) were selected, respectively in LVL and HVL. Functional enrichment analysis indicated DEGs which involved in immune response and apoptosis, played a crucial role in HIV infection. Further, we identified 5 most influential TFs to build miRNA-TF-mRNA regulatory network.
2021-11-10 | GSE140713 | GEO