Hancioglu2007 - Human Immune Response to Influenza A virus Infection

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Model Identifier
BIOMD0000000711
Short description
Format
SBML (L2V4)
Related Publication
  • A dynamical model of human immune response to influenza A virus infection.
  • Hancioglu B, Swigon D, Clermont G
  • Journal of theoretical biology , 5/ 2007 , Volume 246 , Issue 1 , pages: 70-86 , PubMed ID: 17266989
  • Department of Mathematics, 301 Thackeray, University of Pittsburgh, Pittsburgh, PA 15260, USA.
  • We present a simplified dynamical model of immune response to uncomplicated influenza A virus (IAV) infection, which focuses on the control of the infection by the innate and adaptive immunity. Innate immunity is represented by interferon-induced resistance to infection of respiratory epithelial cells and by removal of infected cells by effector cells (cytotoxic T-cells and natural killer cells). Adaptive immunity is represented by virus-specific antibodies. Similar in spirit to the recent model of Bocharov and Romanyukha [1994. Mathematical model of antiviral immune response. III. Influenza A virus infection. J. Theor. Biol. 167, 323-360], the model is constructed as a system of 10 ordinary differential equations with 27 parameters characterizing the rates of various processes contributing to the course of disease. The parameters are derived from published experimental data or estimated so as to reproduce available data about the time course of IAV infection in a na├»ve host. We explore the effect of initial viral load on the severity and duration of the disease, construct a phase diagram that sheds insight into the dynamics of the disease, and perform sensitivity analysis on the model parameters to explore which ones influence the most the onset, duration and severity of infection. To account for the variability and speed of adaptation of the adaptive response to a particular virus strain, we introduce a variable that quantifies the antigenic compatibility between the virus and the antibodies currently produced by the organism. We find that for small initial viral load the disease progresses through an asymptomatic course, for intermediate value it takes a typical course with constant duration and severity of infection but variable onset, and for large initial viral load the disease becomes severe. This behavior is robust to a wide range of parameter values. The absence of antibody response leads to recurrence of disease and appearance of a chronic state with nontrivial constant viral load.
Contributors
Submitter of the first revision: Sarubini Kananathan
Submitter of this revision: Sarubini Kananathan
Modellers: Sarubini Kananathan

Metadata information

is (2 statements)
BioModels Database MODEL1808280004
BioModels Database BIOMD0000000711

isDescribedBy (1 statement)
PubMed 17266989

hasTaxon (2 statements)
Taxonomy Homo sapiens
Taxonomy Influenza A virus

hasProperty (4 statements)
Mathematical Modelling Ontology Ordinary differential equation model
Gene Ontology immune response
Gene Ontology adaptive immune response
Gene Ontology innate immune response


Curation status
Curated


Tags

Connected external resources

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Name Description Size Actions

Model files

Hancioglu2007 - Human Immune Response to Influenza A virus.xml SBML L2V4 representation of Hancioglu2007 - Human Immune Response to Influnza A virus Infection 107.71 KB Preview | Download

Additional files

Hancioglu2007 - Human Immune Response to Influenza A virus.cps Copasi file for the model 151.88 KB Preview | Download

  • Model originally submitted by : Sarubini Kananathan
  • Submitted: Sep 6, 2018 3:24:26 PM
  • Last Modified: Oct 16, 2018 11:37:55 AM
Revisions
  • Version: 7 public model Download this version
    • Submitted on: Oct 16, 2018 11:37:55 AM
    • Submitted by: Sarubini Kananathan
    • With comment: Automatically added model identifier BIOMD0000000711
  • Version: 5 public model Download this version
    • Submitted on: Sep 6, 2018 3:24:26 PM
    • Submitted by: Sarubini Kananathan
    • With comment: PubMed ID Updated

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
APC cells M

trans-3-Hydroxycinnamate
0.0 mmol
Interferons F

Interferon
0.0 mmol
Effector cells E

Effector T-Lymphocyte ; cytotoxic T-lymphocyte
1.0 mmol
Viral Load V

Influenza A Virus ; Influenza A virus
0.01 mmol
Antigenic compatibility S

0002315
0.1 mmol
Dead cells D

Epithelial Cell ; cell death ; Dead
0.0 mmol
Healthy Epithelial cells H

Epithelial Cell
1.0 mmol
Reactions
Reactions Rate Parameters
=> APC_cells__M; Dead_cells__D, Viral_Load__V Respiratory_tract_mucosa*(bMD*Dead_cells__D+bMV*Viral_Load__V)*(1-APC_cells__M) bMV = 0.0037; bMD = 1.0
Interferons__F => Respiratory_tract_mucosa*aF*Interferons__F aF = 8.0
=> Effector_cells__E; APC_cells__M Respiratory_tract_mucosa*bEM*APC_cells__M*Effector_cells__E bEM = 8.3
Viral_Load__V => Respiratory_tract_mucosa*aV1*Viral_Load__V/(1+aV2*Viral_Load__V) aV2 = 23000.0; aV1 = 100.0
=> Antigenic_compatibility__S; Plasma_cells__P Respiratory_tract_mucosa*r*Plasma_cells__P*(1-Antigenic_compatibility__S) r = 3.0E-5
Dead_cells__D = ((1-Healthy_Epithelial_cells__H)-Resistant_cells__R)-Infected_Epithelial_cells__I [] []
Viral_Load__V => ; Healthy_Epithelial_cells__H Respiratory_tract_mucosa*gammaVH*Healthy_Epithelial_cells__H*Viral_Load__V gammaVH = 1.02
Healthy_Epithelial_cells__H => Resistant_cells__R; Interferons__F Respiratory_tract_mucosa*bHF*Interferons__F*Healthy_Epithelial_cells__H bHF = 0.01
Healthy_Epithelial_cells__H => Infected_Epithelial_cells__I; Viral_Load__V Respiratory_tract_mucosa*gammaHV*Viral_Load__V*Healthy_Epithelial_cells__H gammaHV = 0.34
Curator's comment:
(added: 16 Oct 2018, 11:36:22, updated: 16 Oct 2018, 11:36:22)
Figure 2 of the reference publication has been reproduced. Uploaded figure is the first column of sub figures of Figure 2. Initial conditions were taken from the publication. The model was simulated using Copasi 4.22 and the figure was generated using Python 2.7.