Garcia2018basic - cancer and immune cell count basic model

  public model
Model Identifier
BIOMD0000000742
Short description
The paper describes a basic model of immune-tumor cell interactions. 
Created by COPASI 4.25 (Build 207) 
This model is described in the article: 
Cancer-Induced Immunosuppression can enable Effectiveness of Immunotherapy through Bistability Generation: a mathematical and computational Examination 
Victor Garcia, Sebastian Bonhoeffer and Feng Fu 
bioRxiv, 2018 

Abstract: Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can reproduce biologically plausible behavior. This behavior is consistent with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability, where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible, consistent with the notion of an immunological barrier. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into cancer-free state. In combination with the administration of immune effector cells, modifications in cancer cell killing may also be harnessed for immunotherapy without resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers. 

This model is hosted on BioModels Database and identified by: MODEL1907050005. 

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . 
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.
Format
SBML (L3V1)
Related Publication
  • Cancer-Induced Immunosuppression can enable Effectiveness of Immunotherapy through Bistability Generation: a mathematical and computational Examination
  • Victor Garcia, Sebastian Bonhoeffer, Feng Fu
  • bioRxiv , 12/ 2018 , DOI: 10.1101/498741
  • Correspondence: Victor Garcia E-mail address: victor.garcia palencia@alumni.ethz.ch ETH Zurich, Universita ╠łtsstrasse 16, 8092 Zurich, Switzerland
  • Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can reproduce biologically plausible behavior. This behavior is consistent with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability, where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible, consistent with the notion of an immunological barrier. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into cancer-free state. In combination with the administration of immune effector cells, modifications in cancer cell killing may also be harnessed for immunotherapy without resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers.
Contributors
Submitter of the first revision: Jinghao Men
Submitter of this revision: Jinghao Men
Modellers: Jinghao Men

Metadata information

is (2 statements)
BioModels Database MODEL1907050005
BioModels Database BIOMD0000000742

hasTaxon (1 statement)
Taxonomy Homo sapiens

isVersionOf (1 statement)
hasProperty (1 statement)
Mathematical Modelling Ontology Ordinary differential equation model

isDescribedBy (1 statement)

Curation status
Curated



Connected external resources

SBGN view in Newt Editor

Name Description Size Actions

Model files

Garcia2018basic.xml SBML L2V4 representation of tumour and effector cell count basic model 39.56 KB Preview | Download

Additional files

Garcia2018basic.cps CPS file of the model in COPASI 55.84 KB Preview | Download
Garcia2018basic.sedml Auto-generated SEDML file 1.01 KB Preview | Download

  • Model originally submitted by : Jinghao Men
  • Submitted: Jul 9, 2019 3:33:09 PM
  • Last Modified: Jul 9, 2019 3:33:09 PM
Revisions
  • Version: 12 public model Download this version
    • Submitted on: Jul 9, 2019 3:33:09 PM
    • Submitted by: Jinghao Men
    • With comment: Automatically added model identifier BIOMD0000000742
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
T

neoplastic cell
100.0 mmol
E

Effector Immune Cell ; leukocyte
1000.0 mmol
Reactions
Reactions Rate Parameters
T => ; E Tumor*k*T*E k = 1.0E-4 1/ks
=> E; T Tumor*m*E*T m = -1.0E-6 1/ks
=> T Tumor*a*T a = 0.514 1/ks
E => Tumor*d*E d = 0.02 1/ks
=> E Tumor*s s = 10.0 1/ks
T => Tumor*a*b*T*T a = 0.514 1/ks; b = 1.02E-9 1
Curator's comment:
(added: 08 Jul 2019, 14:13:32, updated: 08 Jul 2019, 14:14:03)
Publication figure 3A reproduced as per literature. Figure data is generated using COPASI 4.25 (build 197).