1. Modeling of Acute Resistance to the HER2 Inhibitor, Lapatinib, in Breast Cancer Cells
Marc Fink & Yan Liu & Shangying Wang
Student Project Proposal
Computational Cell Biology 2012

2. Outline Brief review of the project goal
Boolean network model and results
Modeling with ODEs in VCell and COPASI
Analysis of cell survival rate
Summary and outlook

3. Goals
Modeling the signaling pathway of HER2 inhibitor, Lapatinib, in Breast Cancer Cells
Analyze the influence factors of cell apoptosis
Explanation of cell survival rate after treatment
01/13

4. Mechanistic (process) diagrams
Death
Lapatinib
??????
HER2
Survival
PI3K
PDK1 p
AKT (PKB)
Protein
Translation p ER
FoxO p
FoxO p
14-3-3
Translocation
Translocation
Transcription
FoxO
FoxO
Apoptotic
genes
Apoptosis
FoxO
FoxO
Survival
genes
02/13

5. Flow chart and strategies
Lapatinib
HER2
IGF1R
FASL
AKT
FoxO
apoptosis
RAF
MEK
ERK
RSK
BAD
BIM
Lack of experimental parameters => Boolean network
Better understanding of dynamics => ODEs
Analysis of survival rate => Stochastic simulation
03/13

6. Boolean network model
HER2
AKT
FoxO
apoptosis
BIM
Lapatinib
IGF1R
Apoptosis
Time steps
=> Average value of apoptosis is around 0.5 with simplification.
04/13

7. Boolean network model
HER2
FASL
AKT
FoxO
apoptosis
BIM
Lapatinib
IGF1R
Apoptosis
Time steps
=> Average apoptosis is around 0.6 with additional information.
04/13

8. Boolean network model
HER2
FASL
AKT
FoxO
apoptosis
RAF
MEK
ERK
RSK
BAD
BIM
Lapatinib
IGF1R
Apoptosis
Time steps
=> Results depend on the complexity, adding weights not possible.
04/13

9. Modeling with ODEs
=> 22 species and 32 reactions, reasonable rates???!!!
05/13

10. Model reduction and modification
Due to the importance of FOXO => Neglect the downstream and add the self regulation
Lapatinib
HER2
AKT
FoxO
Apoptosis

11. Self regulation of FOXO
FoxO_gene
FoxO_mRNA (x)
FoxO (y)
FoxO* (z) Φ Φ
=> Bistability of the positive feedback loop
06/13

12. Modified model
=> 14 species and 16 reactions
07/13

13. Sensitivity analysis
Binding of Laptinib to HER2
Dimerization of HER2
FOXO
=> Laptinib is important for cancer cell apoptosis
08/13

14. Modeling with ODEs IV
Deterministic simulations with parameter scan (Laptinib)
=> Laptinib is able to stimulate FOXO, crucial to apoptosis
09/13

15. Analysis of cell survival rate
Random initial concentrations (with COPASI)
=> Laptinib is able to stimulate cancer cell apoptosis
10/13

16. Analysis of cell survival rate
Stochastic simulation (with VCell and C)
=> Laptinib is able to stimulate cancer cell apoptosis
11/13

17. Summary and outlook
Apoptosis pathway of breast cancer cell is modeled and analyzed with simplifications
Survival rate of cancer cell is analyzed
Laptinib induced cancer cell apoptosis is with certain probability
Outlook
Improve the pathway model with more details by getting more rates from experiments
Validation of the model and survival rate
12/13

18. Experience with the softwares
COPASI vs VCell
Writing reactions
Checking parameters
Deterministic simulation
Stochastic simulation
Parameter scan
Sensitivity analysis
Visualization
13/13

19. Thank you for your attention!