Gevertz2018 - Cancer Treatment with Oncolytic Viruses and Dendritic Cell injections original model

  public model
Model Identifier
BIOMD0000000816
Short description
The model is based on 'Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections', PMID:30510594. Author:Jana L.Gevertz and Joanna R.Wares. This model describes the original mathematical model described in section 2.1. Built by COPASI 4.24( Build 197) Abstract: Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty.
Format
SBML (L2V4)
Related Publication
  • Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections.
  • Gevertz JL, Wares JR
  • Computational and mathematical methods in medicine , 1/ 2018 , Volume 2018 , pages: 8760371 , PubMed ID: 30510594
  • Department of Mathematics & Statistics, The College of New Jersey, Ewing, New Jersey, USA.
  • Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty.
Contributors
Submitter of the first revision: Szeyi Ng
Submitter of this revision: Szeyi Ng
Modellers: Szeyi Ng

Metadata information

is (2 statements)
BioModels Database BIOMD0000000816
BioModels Database MODEL1909180001

isDescribedBy (1 statement)
PubMed 30510594

hasTaxon (1 statement)
Taxonomy Mus musculus

hasProperty (6 statements)
Experimental Factor Ontology cancer
Mathematical Modelling Ontology Ordinary differential equation model
Human Disease Ontology melanoma
Experimental Factor Ontology melanoma
NCIt Oncolytic Virus Therapy
Brenda Tissue Ontology B16-F10 cell

Curation status
Curated
Modelling approach(es)
ordinary differential equation model
Tags
Immuno-oncology

Connected external resources

SBGN view in Newt Editor

Name Description Size Actions

Model files
model.xml SBML L2V4 file for the model 102.28 KB Preview | Download

Additional files
FIg4a.png Reproduced figure 4(a) 8.42 KB Preview | Download
OV25.sedml Sedml L1V2 file producing figure 4 with OV=2.5x10^9 1.01 KB Preview | Download
OV25.xml SBML L2V4 file for the model with OV=2.5x10^9 99.28 KB Preview | Download
OV5.sedml Sedml L1V2 file producing figure 4 with OV=5x10^9 1.01 KB Preview | Download
OV5.xml SBML L2V4 file for the model with OV=5x10^9 99.28 KB Preview | Download
OVDC.cps COPASI 4.24 (Build 197) file for the model with OV and DC treatment 108.57 KB Preview | Download
OVDC.sedml Sedml L1V2 file producing figure 4 with OV and DC treatment 1.01 KB Preview | Download

  • Model originally submitted by : Szeyi Ng
  • Submitted: Sep 18, 2019 12:07:32 PM
  • Last Modified: Sep 18, 2019 1:57:03 PM
Revisions
  • Version: 5 public model Download this version
    • Submitted on: Sep 18, 2019 1:57:03 PM
    • Submitted by: Szeyi Ng
    • With comment: Edited model metadata online.
  • Version: 3 public model Download this version
    • Submitted on: Sep 18, 2019 12:07:32 PM
    • Submitted by: Szeyi Ng
    • With comment: Automatically added model identifier BIOMD0000000816

(*) 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
Oncolytic Adenovirus V

Adenoviridae ; Oncolytic 		0.0 mmol
Tumor targeting T cells T

Natural Killer T-Cell ; Targeting 		0.0 mmol
Infected Cancer Cell I

cancer ; B16-F10 cell ; Abnormal 		0.0 mmol
Naive T cells A

Natural Killer T-Cell 		0.0 mmol
total tumor cells

B16-F10 cell ; cancer 		58.020755 mmol
Uninfected Tumor Cell U

cancer ; B16-F10 cell 		58.020755 mmol
Reactions
Reactions Rate Parameters
=> Oncolytic_Adenovirus_V compartment*U_V U_V = 0.0
=> Tumor_targeting_T_cells_T; Naive_T_cells_A compartment*chi_A*Naive_T_cells_A chi_A = 1.0
Infected_Cancer_Cell_I => ; Tumor_targeting_T_cells_T, Total_cells_N compartment*(k0+c_kill*Infected_Cancer_Cell_I)*Infected_Cancer_Cell_I*Tumor_targeting_T_cells_T/Total_cells_N k0 = 2.0; c_kill = 0.595397
Tumor_targeting_T_cells_T => compartment*delta_T*Tumor_targeting_T_cells_T delta_T = 0.35
=> Oncolytic_Adenovirus_V; Infected_Cancer_Cell_I compartment*alpha*delta_I*Infected_Cancer_Cell_I alpha = 3.0; delta_I = 1.0
=> Naive_T_cells_A; Infected_Cancer_Cell_I compartment*C_A*Infected_Cancer_Cell_I C_A = 5.17E-4
total_tumor_cells = Uninfected_Tumor_Cell_U+Infected_Cancer_Cell_I [] []
Uninfected_Tumor_Cell_U => Infected_Cancer_Cell_I; Oncolytic_Adenovirus_V, Total_cells_N compartment*beta*Uninfected_Tumor_Cell_U*Oncolytic_Adenovirus_V/Total_cells_N beta = 1.008538
=> Uninfected_Tumor_Cell_U compartment*r*Uninfected_Tumor_Cell_U r = 0.3198
Curator's comment:
(added: 18 Sep 2019, 12:06:10, updated: 18 Sep 2019, 12:06:10)
I reproduced figure 4(a) from the literature. I generated the data using COPASI 4.24(Build 197) and plot the graph using MATLAB. The initial conditions and some other parameters are not indicated very clearly in the paper, I have contacted the author for further information. To view all the parameters, please refer to the attached files.