Murphy2016 - Differences in predictions of ODE models of tumor growth
This model is described in the article:
Abstract:
While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning.
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To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.
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Differences in predictions of ODE models of tumor growth: a cautionary example.
- Murphy H, Jaafari H, Dobrovolny HM
- BMC cancer , 2/ 2016 , Volume 16 , pages: 163 , PubMed ID: 26921070
- Department of Physics, Utica College, Utica, NY, USA. hemurphy@utica.edu.
- While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning.
Submitter of this revision: Emma Fairbanks
Modellers: Emma Fairbanks
Metadata information
isDescribedBy (2 statements)
hasTaxon (1 statement)
isPropertyOf (1 statement)
hasProperty (1 statement)
Connected external resources
SBGN view in Newt Editor
| Name | Description | Size | Actions |
|---|---|---|---|
Model files |
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| BIOMD0000000671_url.xml | SBML L2V4 representation of Murphy2016 - Differences in predictions of ODE models of tumor growth | 27.68 KB | Preview | Download |
Additional files |
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| BIOMD0000000671-biopax2.owl | Auto-generated BioPAX (Level 2) | 4.38 KB | Preview | Download |
| BIOMD0000000671-biopax3.owl | Auto-generated BioPAX (Level 3) | 5.14 KB | Preview | Download |
| BIOMD0000000671.m | Auto-generated Octave file | 4.75 KB | Preview | Download |
| BIOMD0000000671.pdf | Auto-generated PDF file | 137.09 KB | Preview | Download |
| BIOMD0000000671.png | Auto-generated Reaction graph (PNG) | 4.27 KB | Preview | Download |
| BIOMD0000000671.sci | Auto-generated Scilab file | 158.00 Bytes | Preview | Download |
| BIOMD0000000671.svg | Auto-generated Reaction graph (SVG) | 845.00 Bytes | Preview | Download |
| BIOMD0000000671.vcml | Auto-generated VCML file | 900.00 Bytes | Preview | Download |
| BIOMD0000000671.xpp | Auto-generated XPP file | 2.64 KB | Preview | Download |
| BIOMD0000000671_urn.xml | Auto-generated SBML file with URNs | 27.61 KB | Preview | Download |
| MODEL1708250001.cps | Curated and annotated COPASI file | 50.86 KB | Preview | Download |
| MODEL1708250001.sedml | SED-ML file for figure 3 | 4.79 KB | Preview | Download |
- Model originally submitted by : Emma Fairbanks
- Submitted: Aug 25, 2017 10:13:35 AM
- Last Modified: Mar 16, 2018 2:40:56 PM
Revisions
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Version: 2
- Submitted on: Mar 16, 2018 2:40:56 PM
- Submitted by: Emma Fairbanks
- With comment: Current version of BIOMD0000000671
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Version: 1
- Submitted on: Aug 25, 2017 10:13:35 AM
- Submitted by: Emma Fairbanks
- With comment: Original import of BIOMD0000000671
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: Variable used inside SBML models
| Species | Initial Concentration/Amount |
|---|---|
| V log | 220.0 mol |
| V surf | 220.0 mol |
| V gomp | 220.0 mol |
| V bert | 220.0 mol |
| V lin | 220.0 mol |
| V exp Exponential Function |
220.0 mol |
| V mend | 220.0 mol |
| Reactions | Rate | Parameters |
|---|---|---|
| V_log = a_log*V_log*(1-V_log/b_log) | a_log*V_log*(1-V_log/b_log) | b_log = 6920.0; a_log = 0.0295 |
| V_surf = a_surf*V_surf/(V_surf+b_surf)^(1/3) | a_surf*V_surf/(V_surf+b_surf)^(1/3) | a_surf = 0.291; b_surf = 708.0 |
| V_gomp = a_gomp*V_gomp*ln(b_gomp/(V_gomp+c_gomp)) | a_gomp*V_gomp*ln(b_gomp/(V_gomp+c_gomp)) | c_gomp = 10700.0; a_gomp = 0.0919; b_gomp = 15500.0 |
| V_bert = a_bert*V_bert^(2/3)-b_bert*V_bert | a_bert*V_bert^(2/3)-b_bert*V_bert | a_bert = 0.2344; b_bert = 3.46E-19 |
| V_lin = a_lin*V_lin/(V_lin+b_lin) | a_lin*V_lin/(V_lin+b_lin) | b_lin = 4300.0; a_lin = 132.0 |
| V_exp = a_exp*V_exp | a_exp*V_exp | a_exp = 0.0246 |
| V_mend = a_mend*V_mend^b_mend | a_mend*V_mend^b_mend | a_mend = 0.105; b_mend = 0.785 |
(added: 07 Feb 2018, 15:13:23, updated: 07 Feb 2018, 15:13:23)