Sturrock2015 - glioma growth

Model Identifier
BIOMD0000000801
Short description
The paper describes a model of glioma.
Created by COPASI 4.26 (Build 213)
This model is described in the article:
A mathematical model of pre-diagnostic glioma growth
Marc Sturrock, Wenrui Hao, Judith Schwartzbaum, Grzegorz A. Rempala
J Theor Biol. 2015 September 7; 380: 299–308
Abstract:
Due to their location, the malignant gliomas of the brain in humans are very difficult to treat in advanced stages. Blood-based biomarkers for glioma are needed for more accurate evaluation of treatment response as well as early diagnosis. However, biomarker research in primary brain tumors is challenging given their relative rarity and genetic diversity. It is further complicated by variations in the permeability of the blood brain barrier that affects the amount of marker released into the bloodstream. Inspired by recent temporal data indicating a possible decrease in serum glucose levels in patients with gliomas yet to be diagnosed, we present an ordinary differential equation model to capture early stage glioma growth. The model contains glioma-glucose-immune interactions and poses a potential mechanism by which this glucose drop can be explained. We present numerical simulations, parameter sensitivity analysis, linear stability analysis and a numerical experiment whereby we show how a dormant glioma can become malignant.
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Format
SBML
(L3V1)
Related Publication
-
A mathematical model of pre-diagnostic glioma growth.
- Sturrock M, Hao W, Schwartzbaum J, Rempala GA
- Journal of theoretical biology , 9/ 2015 , Volume 380 , pages: 299-308 , PubMed ID: 26073722
- Mathematical Biosciences Institute, The Ohio State University, Columbus 43210, OH, USA.
- Due to their location, the malignant gliomas of the brain in humans are very difficult to treat in advanced stages. Blood-based biomarkers for glioma are needed for more accurate evaluation of treatment response as well as early diagnosis. However, biomarker research in primary brain tumors is challenging given their relative rarity and genetic diversity. It is further complicated by variations in the permeability of the blood brain barrier that affects the amount of marker released into the bloodstream. Inspired by recent temporal data indicating a possible decrease in serum glucose levels in patients with gliomas yet to be diagnosed, we present an ordinary differential equation model to capture early stage glioma growth. The model contains glioma-glucose-immune interactions and poses a potential mechanism by which this glucose drop can be explained. We present numerical simulations, parameter sensitivity analysis, linear stability analysis and a numerical experiment whereby we show how a dormant glioma can become malignant.
Contributors
Submitter of the first revision: Jinghao Men
Submitter of this revision: Jinghao Men
Modellers: Jinghao Men
Submitter of this revision: Jinghao Men
Modellers: Jinghao Men
Metadata information
is (2 statements)
isDescribedBy (1 statement)
hasTaxon (1 statement)
hasProperty (3 statements)
isDescribedBy (1 statement)
hasTaxon (1 statement)
hasProperty (3 statements)
Mathematical Modelling Ontology
Ordinary differential equation model
Gene Ontology immune response to tumor cell
Experimental Factor Ontology glioma
Gene Ontology immune response to tumor cell
Experimental Factor Ontology glioma
Curation status
Curated
Modelling approach(es)
Tags
Connected external resources
Name | Description | Size | Actions |
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Model files |
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Sturrock2015.xml | SBML L3V1 representation of glioma growth model | 78.15 KB | Preview | Download |
Additional files |
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Sturrock2015.cps | CPS file of the model in COPASI | 91.03 KB | Preview | Download |
Sturrock2015.sedml | Auto-generated SEDML file | 3.09 KB | Preview | Download |
- Model originally submitted by : Jinghao Men
- Submitted: Aug 20, 2019 12:52:52 PM
- Last Modified: Aug 20, 2019 12:52:52 PM
Revisions
Legends
: Variable used inside SBML models
: Variable used inside SBML models
Species
Species | Initial Concentration/Amount |
---|---|
T glioma cell |
0.14 mmol |
B glucose |
3.92E-4 mmol |
I leukocyte |
2.84E-4 mmol |
S glucose |
4.39E-4 mmol |
Reactions
Reactions | Rate | Parameters |
---|---|---|
T => ; I | tme*dti*I*T | dti = 0.072 1/d |
T => | tme*dt*T | dt = 1.0E-4 1/d |
B => ; T | tme*dto*T*B | dto = 1.0 1/d |
I => ; T | tme*dtt*T*I | dtt = 0.72 1/d |
B => S | tme*ao*B | ao = 20.0 1/d |
=> I; B | tme*as*(v+I)*B | as = 0.7 1/d; v = 0.7 1 |
S => B | tme*ao*S | ao = 20.0 1/d |
=> I; T | tme*ati*T*I | ati = 3.0E-4 1/d |
Curator's comment:
(added: 20 Aug 2019, 12:52:45, updated: 20 Aug 2019, 12:52:45)
(added: 20 Aug 2019, 12:52:45, updated: 20 Aug 2019, 12:52:45)
Publication figure 2 reproduced as per literature. Figure data is generated using COPASI 4.26 (build 213).