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

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

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

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

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 03/13

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

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

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. 04/13

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

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

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

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

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

Modeling with ODEs IV Deterministic simulations with parameter scan (Laptinib) => Laptinib is able to stimulate FOXO, crucial to apoptosis 09/13

Analysis of cell survival rate Random initial concentrations (with COPASI) => Laptinib is able to stimulate cancer cell apoptosis 10/13

Analysis of cell survival rate Stochastic simulation (with VCell and C) => Laptinib is able to stimulate cancer cell apoptosis 11/13

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

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

Thank you for your attention!