Sun2018 - Instantaneous mutation rate in cancer initiation and progression

  public model
Model Identifier
BIOMD0000000915
Short description
<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1999/xhtml"> <p>Background Cancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes. Results To accurately describe the process of cancer initiation, we propose a new concept of gene initial mutation rate based on our recently designed mathematical model using the non-constant mutation rate. Unlike the widely-used average gene mutation rate that depends on the number of mutations, the gene initial mutation rate can be used to describe the initiation process of a single patient. In addition, we propose the instantaneous tumour doubling time that is a continuous function of time based on the non-constant mutation rate. Our proposed concepts are supported by the clinic data of seven patients with advanced pancreatic cancer. The regression results suggest that, compared with the average mutation rate, the estimated initial mutation rate has a larger value of correlation coefficient with the patient survival time. We also provide the estimated tumour size of these seven patients over time. Conclusions The proposed concepts can be used to describe the cancer initiation and progression for different patients more accurately. Since a quantitative understanding of cancer progression is important for clinical treatment, our proposed model and calculated results may provide insights into the development of treatment schemes and also have other clinic implications.</p> </body> </notes>
Format
SBML (L2V4)
Related Publication
  • Instantaneous mutation rate in cancer initiation and progression.
  • Sun S, Klebaner F, Zhang X, Tian T
  • BMC systems biology , 11/ 2018 , Volume 12 , Issue Suppl 6 , pages: 110 , PubMed ID: 30463617
  • School of Mathematical Sciences, Monash University, Melbourne, 3800, VIC, Australia.
  • BACKGROUND:Cancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes. RESULTS:To accurately describe the process of cancer initiation, we propose a new concept of gene initial mutation rate based on our recently designed mathematical model using the non-constant mutation rate. Unlike the widely-used average gene mutation rate that depends on the number of mutations, the gene initial mutation rate can be used to describe the initiation process of a single patient. In addition, we propose the instantaneous tumour doubling time that is a continuous function of time based on the non-constant mutation rate. Our proposed concepts are supported by the clinic data of seven patients with advanced pancreatic cancer. The regression results suggest that, compared with the average mutation rate, the estimated initial mutation rate has a larger value of correlation coefficient with the patient survival time. We also provide the estimated tumour size of these seven patients over time. CONCLUSIONS:The proposed concepts can be used to describe the cancer initiation and progression for different patients more accurately. Since a quantitative understanding of cancer progression is important for clinical treatment, our proposed model and calculated results may provide insights into the development of treatment schemes and also have other clinic implications.
Contributors
Submitter of the first revision: Szeyi Ng
Submitter of this revision: Ahmad Zyoud
Modellers: Szeyi Ng, Ahmad Zyoud

Metadata information

is (3 statements)
BioModels Database MODEL1909300002
BioModels Database MODEL1909300002
BioModels Database BIOMD0000000915

isDescribedBy (2 statements)
hasTaxon (1 statement)
Taxonomy Homo sapiens

isDerivedFrom (9 statements)
Experimental Factor Ontology pancreatic carcinoma
VariO mutation
Mathematical Modelling Ontology Ordinary differential equation model
BioModels Database MODEL1909300002
Experimental Factor Ontology cancer

occursIn (1 statement)
NCIt Pancreas

hasProperty (5 statements)
VariO mutation
Experimental Factor Ontology pancreatic carcinoma
Experimental Factor Ontology cancer
Mathematical Modelling Ontology Ordinary differential equation model
NCIt Doubling Time

Curation status
Curated
Modelling approach(es)
ordinary differential equation model
Tags

Connected external resources

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Name Description Size Actions

Model files
Sun2018.xml SBML L2V4 file for the model_Curated 75.95 KB Preview | Download

Additional files
Sun2018 - Instantaneous mutation rate in cancer initiation and progression.cps COPASI 4.24 (Build 197) file for the model 60.12 KB Preview | Download
Sun2018 - Instantaneous mutation rate in cancer initiation and progression.xml SBML L2V4 file for the model_Original File 34.41 KB Preview | Download
Sun2018.cps COPASI version 4.27 (Build 217) 116.53 KB Preview | Download
Sun2018.sedml sed-ml L1V2 10.48 KB Preview | Download

  • Model originally submitted by : Szeyi Ng
  • Submitted: Sep 30, 2019 10:23:51 AM
  • Last Modified: Feb 28, 2020 2:17:30 PM
Revisions
  • Version: 5 public model Download this version
    • Submitted on: Feb 28, 2020 2:17:30 PM
    • Submitted by: Ahmad Zyoud
    • With comment: Automatically added model identifier BIOMD0000000915
  • Version: 2 public model Download this version
    • Submitted on: Sep 30, 2019 10:23:51 AM
    • Submitted by: Szeyi Ng
    • With comment: Edited model metadata online.

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Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
p 2

C19700 ; pancreatic carcinoma 		0.0 mmol
p 8

C19700 ; pancreatic carcinoma 		0.0 mmol
p 3

C19700 ; pancreatic carcinoma 		0.0 mmol
p 1

pancreatic carcinoma ; C19700 		0.0 mmol
p 7

C19700 ; pancreatic carcinoma 		0.0 mmol
p 4

C19700 ; pancreatic carcinoma 		0.0 mmol
Reactions
Reactions Rate Parameters
p_2 => p_3 compartment*myu*p_2 myu = 0.001
p_7 => p_8 compartment*myu*p_7 myu = 0.001
p_0 => p_1 compartment*myu*p_0 myu = 0.001
p_3 => p_4 compartment*myu*p_3 myu = 0.001
p_1 => p_2 compartment*myu*p_1 myu = 0.001
p_4 => p_5 compartment*myu*p_4 myu = 0.001
p_6 => p_7 compartment*myu*p_6 myu = 0.001
Curator's comment:
(added: 28 Feb 2020, 14:15:46, updated: 28 Feb 2020, 14:15:46)
The figures that have been reproduced from the curated model we had to change the parameter ( a ) from 0.000001 into 0.001 to be able to reproduce the figures. Figure 1 represent patients 2 and 8 in a constant ( b=0) and non-constant rate parameter ( b = 0.00003) Figure 3 represent the Tumour Doubling time of the model ( DB) and the Tumour doubling time for each patient ( DT_P(1-7))