Hu2019  Modeling Pancreatic Cancer Dynamics with Immunotherapy
Model Identifier
BIOMD0000000792
Short description
This is a mathematical model of pancreatic cancer that includes descriptions of pancreatic cancer cells, pancreatic stellate cells, effector cells and tumorpromoting and tumorsuppressing cytokines to investigate the effects of immunotherapies on patient survival.
Format
SBML
(L2V4)
Related Publication
 Modeling Pancreatic Cancer Dynamics with Immunotherapy.
 Hu X, Ke G, Jang SR
 Bulletin of mathematical biology , 6/ 2019 , Volume 81 , Issue 6 , pages: 18851915 , PubMed ID: 30843136
 Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, 794091042, USA.
 We develop a mathematical model of pancreatic cancer that includes pancreatic cancer cells, pancreatic stellate cells, effector cells and tumorpromoting and tumorsuppressing cytokines to investigate the effects of immunotherapies on patient survival. The model is first validated using the survival data of two clinical trials. Local sensitivity analysis of the parameters indicates there exists a critical activation rate of protumor cytokines beyond which the cancer can be eradicated if four adoptive transfers of immune cells are applied. Optimal control theory is explored as a potential tool for searching the best adoptive cellular immunotherapies. Combined immunotherapies between adoptive ex vivo expanded immune cells and TGF[Formula: see text] inhibition by siRNA treatments are investigated. This study concludes that monoimmunotherapy is unlikely to control the pancreatic cancer and combined immunotherapies between antiTGF[Formula: see text] and adoptive transfers of immune cells can prolong patient survival. We show through numerical explorations that how these two types of immunotherapies are scheduled is important to survival. Applying TGF[Formula: see text] inhibition first followed by adoptive immune cell transfers can yield better survival outcomes.
Contributors
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Rahuman Sheriff
Modellers: Rahuman Sheriff, Johannes Meyer
Submitter of this revision: Rahuman Sheriff
Modellers: Rahuman Sheriff, Johannes Meyer
Metadata information
is (2 statements)
isDescribedBy (1 statement)
hasProperty (2 statements)
isDerivedFrom (1 statement)
isDescribedBy (1 statement)
hasProperty (2 statements)
Mathematical Modelling Ontology
Ordinary differential equation model
Gene Ontology immune response to tumor cell
Gene Ontology immune response to tumor cell
isDerivedFrom (1 statement)
Curation status
Curated
Tags
Connected external resources
Name  Description  Size  Actions 

Model files 

Hu2019.xml  SBML L2V4 Representation of Hu2019  Modeling Pancreatic Cancer Dynamics with Immunotherapy  54.79 KB  Preview  Download 
Additional files 

Hu2019.cps  COPASI file of Hu2019  Modeling Pancreatic Cancer Dynamics with Immunotherapy  93.59 KB  Preview  Download 
Hu2019.sedml  SEDML file of Hu2019  Modeling Pancreatic Cancer Dynamics with Immunotherapy  1.71 KB  Preview  Download 
 Model originally submitted by : Johannes Meyer
 Submitted: Aug 13, 2019 1:43:31 PM
 Last Modified: Oct 5, 2021 9:29:38 AM
Revisions

Version: 4
 Submitted on: Oct 5, 2021 9:29:38 AM
 Submitted by: Rahuman Sheriff
 With comment: Automatically added model identifier BIOMD0000000792

Version: 2
 Submitted on: Aug 13, 2019 1:43:31 PM
 Submitted by: Johannes Meyer
 With comment: Automatically added model identifier BIOMD0000000792
(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions of this model will only be shown to the submitter and their collaborators.
Legends
: Variable used inside SBML models
: Variable used inside SBML models
Species
Species  Initial Concentration/Amount 

w TPC Interleukin6 ; Transforming growth factor beta 1 ; Cytokine 
50000.0 item 
R siRNA Small Interfering RNA 
1.0 item 
z Effector Cells Effector Immune Cell 
1.9E8 item 
x PCC EFO:0002966 ; neoplastic cell 
1.0E9 item 
v TSC Interferon gamma ; Cytokine 
9.4 item 
y PSC pancreatic stellate cell 
5600000.0 item 
Reactions
Reactions  Rate  Parameters 

=> w_TPC; x_PCC, z_Effector_Cells  compartment*v  v=0.1 
=> R_siRNA  compartment*D_0  D_0 = 5.0E10 
=> z_Effector_Cells; v_TSC, w_TPC  compartment*beta_3*z_Effector_Cells*v_TSC/((k_3+v_TSC)*(m_3+w_TPC))  beta_3 = 124.5; m_3 = 1000000.0; k_3 = 2.0E10 
w_TPC =>  compartment*mu_4*w_TPC  mu_4 = 0.034 
=> x_PCC; y_PSC  compartment*(r_1+beta_1*y_PSC)*x_PCC*(1b_1*x_PCC)  r_1 = 0.0195; b_1 = 1.02E11; beta_1 = 1.7857E12 
v_TSC =>  compartment*mu_5*v_TSC  mu_5 = 0.034 
y_PSC =>  compartment*mu_2*y_PSC  mu_2 = 0.015 
=> y_PSC; w_TPC  compartment*(r_2+beta_2*w_TPC/(k_2+w_TPC))*y_PSC*(1b_2*y_PSC)  beta_2 = 0.125; r_2 = 0.00195; k_2 = 5.6E10; b_2 = 1.7857E9 
=> v_TSC; x_PCC, z_Effector_Cells  compartment*beta_5*x_PCC*z_Effector_Cells/(k_5+x_PCC)  k_5 = 1000000.0; beta_5 = 7.3 
x_PCC => ; z_Effector_Cells, w_TPC  compartment*delta_1*x_PCC*z_Effector_Cells/(m_1+w_TPC)  delta_1 = 0.96; m_1 = 1.0E9 
Curator's comment:
(added: 13 Aug 2019, 13:43:19, updated: 13 Aug 2019, 13:43:19)
(added: 13 Aug 2019, 13:43:19, updated: 13 Aug 2019, 13:43:19)
Reproduced plot of Figure 4A (dashed blue line) in the original publication. Parameters and initial conditions are as specified in the paper.
Model simulated and plot produced using COPASI 4.24 (Build 197).