Babbs2012 - immunotherapy

  public model
Model Identifier
BIOMD0000000758
Short description
The paper describes a simple model of tumor immunotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model Charles F Babbs Am J Cancer Res 2012;2(2):204-213 Abstract: The objective of this study was to create a clinically applicable mathematical model of immunotherapy for cancer and use it to explore differences between successful and unsuccessful treatment scenarios. The simplified predator-prey model includes four lumped parameters: tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling factor, λ; and immune cell half-life decay, μ. The predator-prey equations as functions of time, t, for nor- malized tumor cell numbers, y, (the prey) and immunocyte numbers, x, (the predators) are: dy/dt = gy – kx and dx/dt = λxy – μx. A parameter estimation procedure that capitalizes on available clinical data and the timing of clinically observable phenomena gives mid-range benchmarks for parameters representing the unstable equilibrium case in which the tumor neither grows nor shrinks. Departure from this equilibrium results in oscillations in tumor cell num- bers and in many cases complete elimination of the tumor. Several paradoxical phenomena are predicted, including increasing tumor cell numbers prior to a population crash, apparent cure with late recurrence, one or more cycles of tumor growth prior to eventual tumor elimination, and improved tumor killing with initially weaker immune parame- ters or smaller initial populations of immune cells. The model and the parameter estimation techniques are easily adapted to various human cancers that evoke an immune response. They may help clinicians understand and predict certain strange and unexpected effects in the world of tumor immunity and lead to the design of clinical trials to test improved treatment protocols for patients. 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
  • Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model.
  • Babbs CF
  • American journal of cancer research , 1/ 2012 , Volume 2 , Issue 2 , pages: 204-213 , PubMed ID: 22432059
  • Department of Basic Medical Sciences and Weldon School of Biomedical Engineering, Purdue University West Lafayette, Indiana. babbs@purdue.edu
  • The objective of this study was to create a clinically applicable mathematical model of immunotherapy for cancer and use it to explore differences between successful and unsuccessful treatment scenarios. The simplified predator-prey model includes four lumped parameters: tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling factor, λ; and immune cell half-life decay, μ. The predator-prey equations as functions of time, t, for normalized tumor cell numbers, y, (the prey) and immunocyte numbers, ×, (the predators) are: dy/dt = gy - kx and dx/dt = λxy - μx. A parameter estimation procedure that capitalizes on available clinical data and the timing of clinically observable phenomena gives mid-range benchmarks for parameters representing the unstable equilibrium case in which the tumor neither grows nor shrinks. Departure from this equilibrium results in oscillations in tumor cell numbers and in many cases complete elimination of the tumor. Several paradoxical phenomena are predicted, including increasing tumor cell numbers prior to a population crash, apparent cure with late recurrence, one or more cycles of tumor growth prior to eventual tumor elimination, and improved tumor killing with initially weaker immune parameters or smaller initial populations of immune cells. The model and the parameter estimation techniques are easily adapted to various human cancers that evoke an immune response. They may help clinicians understand and predict certain strange and unexpected effects in the world of tumor immunity and lead to the design of clinical trials to test improved treatment protocols for patients.
Contributors
Submitter of the first revision: Jinghao Men
Submitter of this revision: Jinghao Men
Modellers: Jinghao Men

Metadata information

is (2 statements)
BioModels Database MODEL1907240002
BioModels Database BIOMD0000000758

isDescribedBy (1 statement)
PubMed 22432059

hasTaxon (1 statement)
Taxonomy Homo sapiens

hasProperty (3 statements)

Curation status
Curated



Connected external resources

SBGN view in Newt Editor

Name Description Size Actions

Model files

Babbs2012.xml SBML L3V1 representation of the cancer immunotherapy model 31.05 KB Preview | Download

Additional files

Babbs2012.cps CPS file of the model in COPASI 47.23 KB Preview | Download
Babbs2012.sedml Auto-generated SEDML file 1.63 KB Preview | Download

  • Model originally submitted by : Jinghao Men
  • Submitted: Jul 24, 2019 10:13:31 AM
  • Last Modified: Jul 24, 2019 10:13:31 AM
Revisions
  • Version: 3 public model Download this version
    • Submitted on: Jul 24, 2019 10:13:31 AM
    • Submitted by: Jinghao Men
    • With comment: Automatically added model identifier BIOMD0000000758
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
I

effector T cell
0.001 mmol
T

malignant cell ; Tumor Size
1.0 mmol
Reactions
Reactions Rate Parameters
I => tumor_microenvironment*u*I u = 0.1 1/d
=> T tumor_microenvironment*g*T g = 0.004 1/d
=> I; T tumor_microenvironment*l*T*I l = 0.09 1/d
T => ; I tumor_microenvironment*k*I k = 4.0 1/d
Curator's comment:
(added: 24 Jul 2019, 10:13:19, updated: 24 Jul 2019, 10:13:19)
Publication figure 2B reproduced as per literature. Other figures are reproduced with different values of l and k. Figure data is generated using COPASI 4.25 (build 197).