* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download FinalPresentation_ccb2012_marc_shangying_yan
		                    
		                    
								Survey							
                            
		                
		                
                            
                            
								Document related concepts							
                        
                        
                    
						
						
							Transcript						
					
					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 - Analyze the influence factors of cell apoptosis - Explanation of cell survival rate after treatment Outline  Brief review  Boolean network model and results  Modeling with ODEs in VCell and COPASI  Analysis of simulation results  Summary and outlook Mechanistic (process) diagrams Death ?????? Survival Lapatinib HER2 PI3K p PDK1 AKT (PKB) p FoxO p p 14-3-3 FoxO FoxO FoxO FoxO FoxO ER Protein Translation Translocation Transcription Apoptotic genes Survival genes Translocation Apoptosis 01/13 Flow chart and strategies HER2 AKT FoxO Lapatinib IGF1R RAF MEK ERK FASL RSK  Lack of experimental parameters => Boolean network  Better understanding of dynamics => ODEs  Analysis of survival rate => Stochastic simulation BIM BAD apoptosis 02/13 Boolean network model HER2 Lapatinib IGF1R FoxO Apoptosis AKT Time steps BIM apoptosis => Average value of apoptosis is around 0.5 with simplification. 03/13 Boolean network model HER2 Lapatinib IGF1R FoxO Apoptosis AKT FASL Time steps BIM apoptosis => Average apoptosis is around 0.6 with additional information. 03/13 Boolean network model AKT FoxO Lapatinib IGF1R RAF MEK ERK Apoptosis HER2 FASL RSK BIM BAD apoptosis Time steps => Results depend on the complexity, adding weights not possible. 03/13 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 HER2 AKT FoxO Lapatinib IGF1R RAF MEK ERK FASL RSK BIM BAD apoptosis 05/13 Model reduction and modification Due to the importance of FOXO => Neglect the downstream and add the self regulation HER2 AKT Lapatinib HER2_dimer HER2_dimer* PI3K H_PI3K FoxO PIP3 PIP2 AKT Apoptosis Φ FoxO_gene FoxO_mRNA (x) AKT* Φ FoxO (y) FoxO* (z) Model reduction and modification Due to the importance of FOXO => Neglect the downstream and add the self regulation HER2 AKT Lapatinib [Birtwistle et al., 2007] HER2_dimer HER2_dimer* PI3K H_PI3K FoxO PIP3 PIP2 AKT Apoptosis Φ FoxO_gene FoxO_mRNA (x) AKT* Φ FoxO (y) FoxO* (z) Self regulation of FOXO Φ FoxO_gene FoxO_mRNA (x) Φ FoxO (y) => Bistability of the positive feedback loop FoxO* (z) 06/13 Modified model => 14 species and 16 reactions 07/13 Sensitivity analysis in COPASI Binding of Laptinib to HER2 Dimerization of HER2 FOXO => Laptinib is important for cancer cell apoptosis 08/13 Analysis of simulation results  Deterministic simulations with parameter scan (Laptinib) FOXO concentration With increasing initial Laptinib concentration 0 -> 400 nM 09/13 Analysis of simulation results  Deterministic simulations with parameter scan (Laptinib) Phosphorylation => Laptinib is able to stimulate FOXO, crucial to apoptosis 09/13 Analysis of simulation results  Random initial concentrations and constant Laptinib (200nM) FOXO concentration => Initial concentrations influence the effect of Laptinib. 10/13 Analysis of simulation results  Stochastic simulation using Gillespie algorithm (in VCell & C) High Laptinib Low Laptinib 11/13 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 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!