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Modeling of Acute R esistance to the HER2 Inhibitor , L apatinib , in Breast C ancer C ells

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

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Modeling of Acute R esistance to the HER2 Inhibitor , L apatinib , in Breast C ancer C ells

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  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 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 03/13

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

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

  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 04/13

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

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

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

  19. Thank you for your attention!

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