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

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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

Transcript of Analysis of inhibition of HER2 signaling to apoptotic transcription factors

Page 1: 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 WangStudent Project Proposal

Computational Cell Biology 2012

Page 2: Analysis of  inhibition of HER2  signaling to apoptotic transcription factors

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

Page 3: Analysis of  inhibition of HER2  signaling to apoptotic transcription factors

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

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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

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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

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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

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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

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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

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Modeling with ODEs

=> 22 species and 32 reactions, reasonable rates???!!! 04/13

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

05/13

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Model reduction and modification

LapatinibHER2 IGF1R

FASL

AKT

FoxO

apoptosis

RAF

MEK

ERK

RSK

BADBIM

05/13

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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

Page 13: Analysis of  inhibition of HER2  signaling to apoptotic transcription factors

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]

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Self regulation of FOXO

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

06/13=> Bistability of the positive feedback loop

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Modified model

=> 14 species and 16 reactions 07/13

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Sensitivity analysis in COPASI

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

Binding of Laptinib to HER2

FOXODimerization of HER2

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Analysis of simulation resultsDeterministic simulations with parameter scan

(Laptinib)

09/13

With increasing initial Laptinib concentration 0 -> 400 nM

FOXO concentration

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Analysis of simulation resultsDeterministic simulations with parameter scan

(Laptinib)

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

Phosphorylation

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Analysis of simulation resultsRandom initial concentrations and constant Laptinib

(200nM)

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

FOXO concentration

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Analysis of simulation resultsStochastic simulation using Gillespie algorithm (in VCell

& C)

11/13

Low Laptinib

High Laptinib

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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

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

13/13

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Happy Birthday to Nina!