Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law

  public model
Model Identifier
BIOMD0000000850
Short description 
This is a mathematical model using a Gompertz growth law to describe the in vivo dynamics of a cancer under treatment with an oncolytic virus.
Format
SBML (L2V4)
Related Publication
  • Oncolytic virotherapy for tumours following a Gompertz growth law.
  • Jenner AL, Kim PS, Frascoli F
  • Journal of theoretical biology , 11/ 2019 , Volume 480 , pages: 129-140 , PubMed ID: 31400344
  • School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia. Electronic address: a.jenner@maths.usyd.edu.au.
  • Oncolytic viruses are genetically engineered to treat growing tumours and represent a very promising therapeutic strategy. Using a Gompertz growth law, we discuss a model that captures the in vivo dynamics of a cancer under treatment with an oncolytic virus. With the aid of local stability analysis and bifurcation plots, the typical interactions between virus and tumour are investigated. The system shows a singular equilibrium and a number of nonlinear behaviours that have interesting biological consequences, such as long-period oscillations and bistable states where two different outcomes can occur depending on the initial conditions. Complete tumour eradication appears to be possible only for parameter combinations where viral characteristics match well with the tumour growth rate. Interestingly, the model shows that therapies with a high initial injection or involving a highly effective virus do not universally result in successful strategies for eradication. Further, the use of additional, "boosting" injection schedules does not always lead to complete eradication. Our framework, instead, suggests that low viral loads can be in some cases more effective than high loads, and that a less resilient virus can help avoid high amplitude oscillations between tumours and virus. Finally, the model points to a number of interesting findings regarding the role of oscillations and bistable states between a tumour and an oncolytic virus. Strategies for the elimination of such fluctuations depend strongly on the initial viral load and the combination of parameters describing the features of the tumour and virus.
Contributors
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Rahuman Sheriff
Modellers: Rahuman Sheriff, Johannes Meyer

Metadata information

is (2 statements)
BioModels Database BIOMD0000000850
BioModels Database MODEL1911120002

isDescribedBy (1 statement)
PubMed 31400344

isDerivedFrom (1 statement)
PubMed 29644518

hasProperty (2 statements)
Mathematical Modelling Ontology Ordinary differential equation model
NCIt Oncolytic Virus Therapy

Curation status
Curated
Tags
Oncology

Connected external resources

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Name Description Size Actions

Model files
Jenner2019.xml SBML L2V4 Representation of Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law 20.10 KB Preview | Download

Additional files
Jenner2019.cps COPASI file of Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law 43.86 KB Preview | Download
Jenner2019.sedml SED-ML file of Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law 2.67 KB Preview | Download

  • Model originally submitted by : Johannes Meyer
  • Submitted: Nov 12, 2019 11:18:43 AM
  • Last Modified: Oct 5, 2021 8:01:52 PM
Revisions
  • Version: 4 public model Download this version
    • Submitted on: Oct 5, 2021 8:01:52 PM
    • Submitted by: Rahuman Sheriff
    • With comment: Automatically added model identifier BIOMD0000000850
  • Version: 2 public model Download this version
    • Submitted on: Nov 12, 2019 11:18:43 AM
    • Submitted by: Johannes Meyer
    • With comment: Automatically added model identifier BIOMD0000000850

(*) 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
U

neoplastic cell ; uninfected 		75.0 item
V

Oncolytic Virus 		10.0 item
I

neoplastic cell ; infected cell 		100.0 item
Reactions
Reactions Rate Parameters
=> U compartment*m*ln(K/U)*U m = 0.1; K = 100.0
U => I; V compartment*U*V/(U+I) []
I => V compartment*xi*I xi = 0.01
V => compartment*gamma*V gamma = 0.1
Curator's comment:
(added: 12 Nov 2019, 11:18:34, updated: 12 Nov 2019, 11:18:34)
Reproduced plot of Figure 3(1) in the original publication. Model simulated and plot produced using COPASI 4.24 (Build 197).