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Analysis of inhibition of HER2 signaling to apoptotic transcription factors

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

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Analysis of inhibition of HER2 signaling to apoptotic transcription factors

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  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 IGF1R Lapatinib HER2 • Lack of experimental parameters => Boolean network • Better understanding of dynamics => ODEs • Analysis of survival rate => Stochastic simulation RAF AKT MEK FoxO ERK FASL RSK BIM BAD apoptosis 02/13

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

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

  8. Boolean network model IGF1R Lapatinib HER2 RAF AKT MEK FoxO Apoptosis ERK FASL Time steps RSK => Results depend on the complexity, adding weights not possible. BIM BAD apoptosis 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 IGF1R Lapatinib HER2 RAF AKT MEK FoxO ERK FASL RSK BIM BAD apoptosis 05/13

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

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

  14. Self regulation of FOXO Φ Φ FoxO* (z) FoxO_mRNA (x) FoxO_gene FoxO (y) => 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 vsVCell • Writing reactions + +++ • Checking parameters + +++ • Deterministic simulation +++ + • Stochastic simulation ++ + • Parameter scan +++ ++ • Sensitivity analysis +++ - • Visualization - +++ 13/13

  23. Happy Birthday to Nina!

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