analysis of inhibition of her2 signaling to apoptotic transcription factors
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Analysis of inhibition of HER2 signaling to apoptotic transcription factors. Marc Fink & Yan Liu & Shangying Wang Student Project Proposal Computational Cell Biology 2012. Goals. Modeling the signaling pathway of HER2 inhibitor, Lapatinib , in Breast Cancer Cells - PowerPoint PPT PresentationTRANSCRIPT
Analysis of inhibition of HER2 signaling to apoptotic transcription
factors
Marc Fink & Yan Liu & Shangying WangStudent Project Proposal
Computational Cell Biology 2012
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
OutlineBrief reviewBoolean network model and resultsModeling with ODEs in VCell and COPASIAnalysis of simulation results Summary and outlook
Mechanistic (process) diagrams
HER2
PDK1
AKT (PKB)
14-3-3FoxOFoxO
PI3K
p
pp
p Translocation
FoxOFoxO Apoptotic genes
Transcription
Translocation
ProteinTranslation
FoxOFoxO Survival genes
DeathSurvival
??????
ER
Lapatinib
Apoptosis
01/13
Flow chart and strategies Lack of experimental
parameters => Boolean network
Better understanding of dynamics => ODEs
Analysis of survival rate => Stochastic simulation
02/13
LapatinibHER2 IGF1R
FASL
AKT
FoxO
apoptosis
RAF
MEK
ERK
RSK
BADBIM
Boolean network model
=> Average value of apoptosis is around 0.5 with simplification.
HER2
AKT
FoxO
apoptosis
BIMAp
opto
sisTime steps
03/13
Lapatinib IGF1R
Boolean network modelHER2
FASL
AKT
FoxO
apoptosis
BIM => Average apoptosis is around 0.6 with additional information.
Apop
tosis
Time steps
03/13
Lapatinib IGF1R
Boolean network modelHER2
FASL
AKT
FoxO
apoptosis
RAF
MEK
ERK
RSK
BADBIM => Results depend on
the complexity, adding weights not possible.
Apop
tosis
Time steps
03/13
Lapatinib IGF1R
Modeling with ODEs
=> 22 species and 32 reactions, reasonable rates???!!! 04/13
Model reduction and modification Due to the importance of FOXO => Neglect the downstream and add the self regulation
05/13
Model reduction and modification
LapatinibHER2 IGF1R
FASL
AKT
FoxO
apoptosis
RAF
MEK
ERK
RSK
BADBIM
05/13
Model reduction and modification
LapatinibHER2
AKT
FoxO
Due to the importance of FOXO => Neglect the downstream and add the self regulation
Apoptosis AKT
HER2_dimer
HER2_dimer* PI3K
H_PI3K
PIP2 PIP3
AKT*
FoxO_gene FoxO_mRNA (x) FoxO (y) FoxO* (z)Φ Φ
Model reduction and modification
LapatinibHER2
AKT
FoxO
Due to the importance of FOXO => Neglect the downstream and add the self regulation
Apoptosis AKT
HER2_dimer
HER2_dimer* PI3K
H_PI3K
PIP2 PIP3
AKT*
FoxO_gene FoxO_mRNA (x) FoxO (y) FoxO* (z)Φ Φ
[Birtwistle et al., 2007]
Self regulation of FOXO
FoxO_gene FoxO_mRNA (x) FoxO (y) FoxO* (z)Φ Φ
06/13=> Bistability of the positive feedback loop
Modified model
=> 14 species and 16 reactions 07/13
Sensitivity analysis in COPASI
=> Laptinib is important for cancer cell apoptosis 08/13
Binding of Laptinib to HER2
FOXODimerization of HER2
Analysis of simulation resultsDeterministic simulations with parameter scan
(Laptinib)
09/13
With increasing initial Laptinib concentration 0 -> 400 nM
FOXO concentration
Analysis of simulation resultsDeterministic simulations with parameter scan
(Laptinib)
=> Laptinib is able to stimulate FOXO, crucial to apoptosis 09/13
Phosphorylation
Analysis of simulation resultsRandom initial concentrations and constant Laptinib
(200nM)
=> Initial concentrations influence the effect of Laptinib. 10/13
FOXO concentration
Analysis of simulation resultsStochastic simulation using Gillespie algorithm (in VCell
& C)
11/13
Low Laptinib
High Laptinib
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 experimentsMeasurement of concentrations within small time scale
before and after treatment will help to understand the whole signaling process and validate the model. 12/13
Experience with the softwares COPASI vs VCell Writing reactions + +++Checking parameters + +++Deterministic simulation +++ +Stochastic simulation ++ + Parameter scan +++ ++Sensitivity analysis +++ -Visualization - +++
13/13
Happy Birthday to Nina!