Wodarz2018/1 - simple model

  public model
Model Identifier
BIOMD0000000774
Short description
The paper describes a basic model of effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells. Created by COPASI 4.25 (Build 207) This model is described in the article: Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulation Dominik Wodarz J Theor Biol. 2018 July 07; 448: 86–93 Abstract: Tissues are maintained by adult stem cells that self-renew and also differentiate into functioning tissue cells. Homeostasis is achieved by a set of complex mechanisms that involve regulatory feedback loops. Similarly, tumors are believed to be maintained by a minority population of cancer stem cells, while the bulk of the tumor is made up of more differentiated cells, and there is indication that some of the feedback loops that operate in tissues continue to be functional in tumors. Mathematical models of such tissue hierarchies, including feedback loops, have been analyzed in a variety of different contexts. Apart from stem cells giving rise to differentiated cells, it has also been observed that more differentiated cells can de-differentiate into stem cells, both in healthy tissue and tumors, aspects of which have also been investigated mathematically. This paper analyses the effect of de-differentiation on the basic and evolutionary dynamics of cells in the context of tissue hierarchy models that include negative feedback regulation of the cell populations. The models predict that in the presence of de-differentiation, the fixation probability of a neutral mutant is lower than in its absence. Therefore, if de-differentiation occurs, a mutant with identical parameters compared to the wild-type cell population behaves like a disadvantageous mutant. Similarly, the process of de-differentiation is found to lower the fixation probability of an advantageous mutant. These results indicate that the presence of de- differentiation can lower the rates of tumor initiation and progression in the context of the models considered here. 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
  • Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulation.
  • Wodarz D
  • Journal of theoretical biology , 7/ 2018 , Volume 448 , pages: 86-93 , PubMed ID: 29605227
  • Department of Ecology and Evolutionary Biology & Department of Mathematics, 321 Steinhaus Hall, University of California, Irvine, CA 92617, USA. Electronic address: dwodarz@uci.edu.
  • Tissues are maintained by adult stem cells that self-renew and also differentiate into functioning tissue cells. Homeostasis is achieved by a set of complex mechanisms that involve regulatory feedback loops. Similarly, tumors are believed to be maintained by a minority population of cancer stem cells, while the bulk of the tumor is made up of more differentiated cells, and there is indication that some of the feedback loops that operate in tissues continue to be functional in tumors. Mathematical models of such tissue hierarchies, including feedback loops, have been analyzed in a variety of different contexts. Apart from stem cells giving rise to differentiated cells, it has also been observed that more differentiated cells can de-differentiate into stem cells, both in healthy tissue and tumors, aspects of which have also been investigated mathematically. This paper analyses the effect of de-differentiation on the basic and evolutionary dynamics of cells in the context of tissue hierarchy models that include negative feedback regulation of the cell populations. The models predict that in the presence of de-differentiation, the fixation probability of a neutral mutant is lower than in its absence. Therefore, if de-differentiation occurs, a mutant with identical parameters compared to the wild-type cell population behaves like a disadvantageous mutant. Similarly, the process of de-differentiation is found to lower the fixation probability of an advantageous mutant. These results indicate that the presence of de-differentiation can lower the rates of tumor initiation and progression in the context of the models considered here.
Contributors
Submitter of the first revision: Jinghao Men
Submitter of this revision: Jinghao Men
Modellers: Jinghao Men

Metadata information

is (2 statements)
BioModels Database BIOMD0000000774
BioModels Database MODEL1908010003

isDescribedBy (1 statement)
PubMed 29605227

hasTaxon (1 statement)
Taxonomy Homo sapiens

hasProperty (2 statements)
Mathematical Modelling Ontology Ordinary differential equation model
Gene Ontology immune response to tumor cell


Curation status
Curated


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Connected external resources

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Model files

Wodarz2018:1.xml SBML L3V1 representation of simple model 43.13 KB Preview | Download

Additional files

Wodarz2018:1.cps CPS file of the model in COPASI 55.54 KB Preview | Download
Wodarz2018:1.sedml Auto-generated SEDML file 2.12 KB Preview | Download

  • Model originally submitted by : Jinghao Men
  • Submitted: Aug 1, 2019 3:07:18 PM
  • Last Modified: Aug 1, 2019 3:07:18 PM
Revisions
  • Version: 3 public model Download this version
    • Submitted on: Aug 1, 2019 3:07:18 PM
    • Submitted by: Jinghao Men
    • With comment: Automatically added model identifier BIOMD0000000774
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
D

cell
0.0 mmol
S

stem cell
1.0 mmol
Reactions
Reactions Rate Parameters
D => tme*a*D a = 0.0025 1
D => S tme*g*D g = 0.00346534653465347 1
=> S tme*r*S*(2*p-1) p = 0.7 1; r = 0.01 1
=> D; S tme*2*r*S*(1-p) p = 0.7 1; r = 0.01 1
Curator's comment:
(added: 01 Aug 2019, 15:07:11, updated: 01 Aug 2019, 15:07:11)
Publication figure 1a reproduced as per literature. Figure data is generated using COPASI 4.25 (build 197).