Analysis of inhibition of HER2 signaling to apoptotic transcription factors

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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 - PowerPoint PPT Presentation

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Analysis of inhibition of HER2 signaling to apoptotic transcription

factors

Marc Fink & Yan Liu & Shangying WangStudent 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

OutlineBrief reviewBoolean network model and resultsModeling with ODEs in VCell and COPASIAnalysis of simulation results Summary and outlook

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

01/13

Flow chart and strategies Lack of experimental

parameters => Boolean network

Better understanding of dynamics => ODEs

Analysis of survival rate => Stochastic simulation

02/13

LapatinibHER2 IGF1R

FASL

AKT

FoxO

apoptosis

RAF

MEK

ERK

RSK

BADBIM

Boolean network model

=> Average value of apoptosis is around 0.5 with simplification.

HER2

AKT

FoxO

apoptosis

BIMAp

opto

sisTime steps

03/13

Lapatinib IGF1R

Boolean network modelHER2

FASL

AKT

FoxO

apoptosis

BIM => Average apoptosis is around 0.6 with additional information.

Apop

tosis

Time steps

03/13

Lapatinib IGF1R

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

03/13

Lapatinib IGF1R

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

LapatinibHER2 IGF1R

FASL

AKT

FoxO

apoptosis

RAF

MEK

ERK

RSK

BADBIM

05/13

Model reduction and modification

LapatinibHER2

AKT

FoxO

Due to the importance of FOXO => Neglect the downstream and add the self regulation

Apoptosis AKT

HER2_dimer

HER2_dimer* PI3K

H_PI3K

PIP2 PIP3

AKT*

FoxO_gene FoxO_mRNA (x) FoxO (y) FoxO* (z)Φ Φ

Model reduction and modification

LapatinibHER2

AKT

FoxO

Due to the importance of FOXO => Neglect the downstream and add the self regulation

Apoptosis AKT

HER2_dimer

HER2_dimer* PI3K

H_PI3K

PIP2 PIP3

AKT*

FoxO_gene FoxO_mRNA (x) FoxO (y) FoxO* (z)Φ Φ

[Birtwistle et al., 2007]

Self regulation of FOXO

FoxO_gene FoxO_mRNA (x) FoxO (y) FoxO* (z)Φ Φ

06/13=> Bistability of the positive feedback loop

Modified model

=> 14 species and 16 reactions 07/13

Sensitivity analysis in COPASI

=> Laptinib is important for cancer cell apoptosis 08/13

Binding of Laptinib to HER2

FOXODimerization of HER2

Analysis of simulation resultsDeterministic simulations with parameter scan

(Laptinib)

09/13

With increasing initial Laptinib concentration 0 -> 400 nM

FOXO concentration

Analysis of simulation resultsDeterministic simulations with parameter scan

(Laptinib)

=> Laptinib is able to stimulate FOXO, crucial to apoptosis 09/13

Phosphorylation

Analysis of simulation resultsRandom initial concentrations and constant Laptinib

(200nM)

=> Initial concentrations influence the effect of Laptinib. 10/13

FOXO concentration

Analysis of simulation resultsStochastic simulation using Gillespie algorithm (in VCell

& C)

11/13

Low Laptinib

High Laptinib

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 experimentsMeasurement 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!