Figueredo2013/1 - immunointeraction base model

  public model
Model Identifier
BIOMD0000000753
Short description
The paper describes a basic model of immune-itumor interaction. Created by COPASI 4.25 (Build 207) This model is described in the article: Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective Grazziela P Figueredo, Peer-Olaf Siebers, Uwe Aickelin Kathleen BMC Bioinformatics 2013, 14(Suppl 6):S6 Abstract: Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena. 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
  • Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective.
  • Figueredo GP, Siebers PO, Aickelin U
  • BMC bioinformatics , 1/ 2013 , Volume 14 Suppl 6 , pages: S6 , PubMed ID: 23734575
  • Intelligent Modelling and Analysis Research Group, School of Computer Science, The University of Nottingham, UK. grazziela.figueredo@nottingham.ac.uk
  • Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.
Contributors
Submitter of the first revision: Jinghao Men
Submitter of this revision: Jinghao Men
Modellers: Jinghao Men

Metadata information

is (2 statements)
BioModels Database BIOMD0000000753
BioModels Database MODEL1907180001

isDescribedBy (1 statement)
PubMed 23734575

hasTaxon (1 statement)
Taxonomy Homo sapiens

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


Curation status
Curated



Connected external resources

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Name Description Size Actions

Model files

Figueredo2013:1.xml SBML L3V1 representation of the immunointeraction basic model 44.68 KB Preview | Download

Additional files

Figueredo2013:1.cps CPS file of the model in COPASI 60.28 KB Preview | Download
Figueredo2013:1.sedml Auto-generated SEDML file 2.13 KB Preview | Download

  • Model originally submitted by : Jinghao Men
  • Submitted: Jul 18, 2019 11:22:08 AM
  • Last Modified: Jul 18, 2019 11:22:08 AM
Revisions
  • Version: 3 public model Download this version
    • Submitted on: Jul 18, 2019 11:22:08 AM
    • Submitted by: Jinghao Men
    • With comment: Automatically added model identifier BIOMD0000000753
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
T

malignant cell
50.0 mmol
E

Effector Immune Cell
5.0 mmol
Reactions
Reactions Rate Parameters
T => tumor_microenvironment*a*b*T*T a = 1.636 1; b = 0.004 1
E => ; T tumor_microenvironment*m*E*T m = 0.00311 1
T => ; E tumor_microenvironment*n*T*E n = 1.0 1
=> E; T tumor_microenvironment*p*T*E/(g+T) g = 20.19 1; p = 1.131 1
E => tumor_microenvironment*d*E d = 2.0 1
=> E tumor_microenvironment*s s = 0.318 1
=> T tumor_microenvironment*a*T a = 1.636 1
Curator's comment:
(added: 18 Jul 2019, 11:21:50, updated: 18 Jul 2019, 11:21:50)
Publication figure 4a reproduced as per literature. Other figures are reproduced with different sets of parameters. Figure data is generated using COPASI 4.25 (build 197).