1. Analysis of inhibition of HER2 signaling to apoptotic transcription factors
Marc Fink & Yan Liu & Shangying Wang
Student Project Proposal
Computational Cell Biology 2012

2. 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

3. Outline Brief review Boolean network model and results
Modeling with ODEs in VCell and COPASI
Analysis of simulation results
Summary and outlook

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
01/13

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
02/13

6. Boolean network model
HER2
AKT
FoxO
apoptosis
BIM
Lapatinib
IGF1R
Apoptosis
Time steps
=> Average value of apoptosis is around 0.5 with simplification.
03/13

7. Boolean network model
HER2
FASL
AKT
FoxO
apoptosis
BIM
Lapatinib
IGF1R
Apoptosis
Time steps
=> Average apoptosis is around 0.6 with additional information.
03/13

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.
03/13

9. Modeling with ODEs
=> 22 species and 32 reactions, reasonable rates???!!!
04/13

10. Model reduction and modification
Due to the importance of FOXO => Neglect the downstream and add the self regulation
05/13

11. Model reduction and modification
Lapatinib
HER2
IGF1R
FASL
AKT
FoxO
apoptosis
RAF
MEK
ERK
RSK
BAD
BIM
05/13

12. Model reduction and modification
Due to the importance of FOXO => Neglect the downstream and add the self regulation
AKT
HER2_dimer
HER2_dimer*
PI3K
H_PI3K
PIP2
PIP3
AKT*
Lapatinib
HER2
AKT
FoxO
Apoptosis
FoxO_gene
FoxO_mRNA (x)
FoxO (y)
FoxO* (z) Φ

13. Model reduction and modification
Due to the importance of FOXO => Neglect the downstream and add the self regulation
AKT
HER2_dimer
HER2_dimer*
PI3K
H_PI3K
PIP2
PIP3
AKT*
Lapatinib
HER2
AKT
FoxO
[Birtwistle et al., 2007]
Apoptosis
FoxO_gene
FoxO_mRNA (x)
FoxO (y)
FoxO* (z) Φ

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

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

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

17. Analysis of simulation results
Deterministic simulations with parameter scan (Laptinib)
FOXO concentration
With increasing initial Laptinib concentration
0 -> 400 nM
09/13

18. Analysis of simulation results
Deterministic simulations with parameter scan (Laptinib)
Phosphorylation
=> Laptinib is able to stimulate FOXO, crucial to apoptosis
09/13

19. Analysis of simulation results
Random initial concentrations and constant Laptinib (200nM)
FOXO concentration
=> Initial concentrations influence the effect of Laptinib.
10/13

20. Analysis of simulation results
Stochastic simulation using Gillespie algorithm (in VCell & C)
High Laptinib
Low Laptinib
11/13

21. Summary and outlook
Inhibition of HER2 signaling to apoptotic transcription factors is studied.
Models with different complexities are analyzed.
Laptinib induced inhibition of HER2 is simulated.
Outlook
Improve the stochastic study
Improve the pathway model with more details by getting more rates from experiments
Measurement of concentrations within small time scale before and after treatment will help to understand the whole signaling process and validate the model.
12/13

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

23. Happy Birthday to Nina!