modeling of acute r esistance to the her2 inhibitor , l apatinib , in breast c ancer c ells
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Modeling of Acute R esistance to the HER2 Inhibitor , L apatinib , in Breast C ancer C ells. Marc Fink & Yan Liu & Shangying Wang Student Project Proposal Computational Cell Biology 2012. Outline. Brief review of the project goal Boolean network model and results - PowerPoint PPT PresentationTRANSCRIPT
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Modeling of Acute Resistance to the HER2 Inhibitor, Lapatinib, in
Breast Cancer Cells
Marc Fink & Yan Liu & Shangying WangStudent Project Proposal
Computational Cell Biology 2012
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OutlineBrief review of the project goalBoolean network model and resultsModeling with ODEs in VCell and COPASIAnalysis of cell survival rateSummary and outlook
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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
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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
02/13
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Flow chart and strategies Lack of experimental
parameters => Boolean network
Better understanding of dynamics => ODEs
Analysis of survival rate => Stochastic simulation
03/13
LapatinibHER2 IGF1R
FASL
AKT
FoxO
apoptosis
RAF
MEK
ERK
RSK
BADBIM
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Boolean network model
=> Average value of apoptosis is around 0.5 with simplification.
HER2
AKT
FoxO
apoptosis
BIMAp
opto
sisTime steps
04/13
Lapatinib IGF1R
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Boolean network modelHER2
FASL
AKT
FoxO
apoptosis
BIM => Average apoptosis is around 0.6 with additional information.
Apop
tosis
Time steps
04/13
Lapatinib IGF1R
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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
04/13
Lapatinib IGF1R
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Modeling with ODEs
=> 22 species and 32 reactions, reasonable rates???!!! 05/13
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Model reduction and modification
LapatinibHER2
AKT
FoxO
Due to the importance of FOXO => Neglect the downstream and add the self regulation
Apoptosis
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Self regulation of FOXO
FoxO_gene FoxO_mRNA (x) FoxO (y) FoxO* (z)Φ Φ
06/13=> Bistability of the positive feedback loop
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Modified model
=> 14 species and 16 reactions 07/13
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Sensitivity analysis
=> Laptinib is important for cancer cell apoptosis 08/13
Binding of Laptinib to HER2
FOXODimerization of HER2
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Modeling with ODEs IVDeterministic simulations with parameter scan (Laptinib)
=> Laptinib is able to stimulate FOXO, crucial to apoptosis 09/13
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Analysis of cell survival rateRandom initial concentrations (with COPASI)
=> Laptinib is able to stimulate cancer cell apoptosis 10/13
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Analysis of cell survival rateStochastic simulation (with VCell and C)
=> Laptinib is able to stimulate cancer cell apoptosis 11/13
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Summary and outlookApoptosis pathway of breast cancer cell is modeled
and analyzed with simplificationsSurvival rate of cancer cell is analyzed Laptinib induced cancer cell apoptosis is with
certain probabilityOutlookImprove the pathway model with more details by
getting more rates from experimentsValidation of the model and survival rate
12/13
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Experience with the softwares COPASI vs VCell Writing reactions + +++Checking parameters + +++Deterministic simulation +++ +Stochastic simulation ++ + Parameter scan +++ ++Sensitivity analysis +++ -Visualization - +++
13/13
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Thank you for your attention!