Fassoni2019  Oncogenesis encompassing mutations and genetic instability
Short description
This model describes the multistep process that transform a normal cell and its descendants into a malignant tumour by considering three populations: normal, premalignant and cancer cells. Created by COPASI 4.24(Build 197)
Abstract:
Tumorigenesis has been described as a multistep process, where each step is associated with a genetic alteration, in the direction to progressively transform a normal cell and its descendants into a malignant tumour. Into this work, we propose a mathematical model for cancer onset and development, considering three populations: normal, premalignant and cancer cells. The model takes into account three hallmarks of cancer: selfsufficiency on growth signals, insensibility to antigrowth signals and evading apoptosis. By using a nonlinear expression to describe the mutation from premalignant to cancer cells, the model includes genetic instability as an enabling characteristic of tumour progression. Mathematical analysis was performed in detail. Results indicate that apoptosis and tissue repair system are the first barriers against tumour progression. One of these mechanisms must be corrupted for cancer to develop from a single mutant cell. The results also show that the presence of aggressive cancer cells opens way to survival of less adapted premalignant cells. Numerical simulations were performed with parameter values based on experimental data of breast cancer, and the necessary time taken for cancer to reach a detectable size from a single mutant cell was estimated with respect to some parameters. We find that the rates of apoptosis and mutations have a large influence on the pace of tumour progression and on the time it takes to become clinically detectable.
Format
SBML
(L3V1)
Related Publication
 Modeling dynamics for oncogenesis encompassing mutations and genetic instability.
 Fassoni AC, Yang HM
 Mathematical medicine and biology : a journal of the IMA , 6/ 2019 , Volume 36 , Issue 2 , pages: 241267
 Instituto de Matemática e Computação, UNIFEI, Itajubá, Minas Gerais, Brazil.
 Tumorigenesis has been described as a multistep process, where each step is associated with a genetic alteration, in the direction to progressively transform a normal cell and its descendants into a malignant tumour. Into this work, we propose a mathematical model for cancer onset and development, considering three populations: normal, premalignant and cancer cells. The model takes into account three hallmarks of cancer: selfsufficiency on growth signals, insensibility to antigrowth signals and evading apoptosis. By using a nonlinear expression to describe the mutation from premalignant to cancer cells, the model includes genetic instability as an enabling characteristic of tumour progression. Mathematical analysis was performed in detail. Results indicate that apoptosis and tissue repair system are the first barriers against tumour progression. One of these mechanisms must be corrupted for cancer to develop from a single mutant cell. The results also show that the presence of aggressive cancer cells opens way to survival of less adapted premalignant cells. Numerical simulations were performed with parameter values based on experimental data of breast cancer, and the necessary time taken for cancer to reach a detectable size from a single mutant cell was estimated with respect to some parameters. We find that the rates of apoptosis and mutations have a large influence on the pace of tumour progression and on the time it takes to become clinically detectable.
Contributors
Szeyi Ng
Metadata information
hasProperty
isDescribedBy
Curation status
Curated
Modelling approach(es)
Tags
Name  Description  Size  Actions 

Model files 

model.xml  SBML L3V1 file for the model  97.00 KB  Preview  Download 
Additional files 

5C.sedml  Sedml L1V2 file producing figure 5(C)  7.37 KB  Preview  Download 
Figure 5.png  PNG plot of the model simulation Fig.5  95.69 KB  Preview  Download 
5D.sedml  Sedml L1V2 file producing figure 5(D)  7.37 KB  Preview  Download 
5B.sedml  Sedml L1V2 file producing figure 5(B)  7.37 KB  Preview  Download 
Fassoni2019  Oncogenesis encompassing mutations and genetic instability.cps  COPASI 4.24 (Build 197) file for the model  112.14 KB  Preview  Download 
5A.sedml  Sedml L1V2 file producing figure 5(A)  5.19 KB  Preview  Download 
 Model originally submitted by : Szeyi Ng
 Submitted: 05Sep2019 14:29:47
 Last Modified: 11Sep2019 14:44:15
Revisions

Version: 5
 Submitted on: 11Sep2019 14:44:15
 Submitted by: Szeyi Ng
 With comment: Edited model metadata online.

Version: 3
 Submitted on: 05Sep2019 14:44:42
 Submitted by: Szeyi Ng
 With comment: Automatically added model identifier BIOMD0000000807

Version: 2
 Submitted on: 05Sep2019 14:29:47
 Submitted by: Szeyi Ng
 With comment: Edited model metadata online.
Curator's comment:
(added: 05 Sep 2019, 14:42:30, updated: 05 Sep 2019, 14:42:30)
(added: 05 Sep 2019, 14:42:30, updated: 05 Sep 2019, 14:42:30)
All the reproduced figures are plotted using COPASI.
Fig 5A with 5A.sedml and model.xml xi_A=0.006 and beta_3=0.35x10^(9)
Fig 5B with 5B.sedml and model.xml xi_A=0.003 and beta_3=0.35x10^(9)
Fig 5C with 5C.sedml and model.xml xi_A=0 and beta_3=0.35x10^(9)
Fig 5D with 5D.sedml and model.xml xi_A=0 and beta_3=0.28x10^(9)
Professor Fassoni has confirmed us with some parameters used to reproduce the figures:
Because the different orders of magnitude involved( large populations and small parameters), to simulate it, we used the normalized (nondimensional) populations.
So in the figure we plotted, we used :n(t)=N(t)/K, G(t)=G(t)/K, a(t)=A(t)/K and n(t),g(t),a(t) follow the same ODEs of N(t),G(t),A(t) (system 2.4).
We have the following replacements in parameters:
 r_N is replaced by r_N/K
 K_A is replaced by K_A/K
 \xi is replaced by \xi/K
 \beta_i is replaced by \beta_i * K (i=1,2,3)
 the other parameters remain the same
 the initial conditions, which are N(0)=(r_N/\mu_N)1, G(0)=1, A(0)=0, are replaced by n(0)=N(0)/K, g(0)=G(0)/K, a(0)=A(0)/K
We used the value K=10^8.
Correction for parameter K_A in table 1 of the paper: K_A=10^7