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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

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
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
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
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
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

boolean network model
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

boolean network model1
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

boolean network model2
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

modeling with odes
Modeling with ODEs

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

05/13

model reduction and modification
Model reduction and modification

Lapatinib

HER2

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

AKT

FoxO

Apoptosis

self regulation of foxo
Self regulation of FOXO

Φ

Φ

FoxO* (z)

FoxO_mRNA (x)

FoxO_gene

FoxO (y)

=> Bistability of the positive feedback loop

06/13

modified model
Modified model

=> 14 species and 16 reactions

07/13

sensitivity analysis
Sensitivity analysis

Binding of Laptinib to HER2

Dimerization of HER2

FOXO

=> Laptinib is important for cancer cell apoptosis

08/13

modeling with odes iv
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
Analysis of cell survival rate
  • Random initial concentrations (with COPASI)

=> Laptinib is able to stimulate cancer cell apoptosis

10/13

analysis of cell survival rate1
Analysis of cell survival rate
  • Stochastic simulation (with VCell and C)

=> Laptinib is able to stimulate cancer cell apoptosis

11/13

summary and outlook
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
Experience with the softwares

COPASI vsVCell

  • Writing reactions + +++
  • Checking parameters + +++
  • Deterministic simulation +++ +
  • Stochastic simulation ++ +
  • Parameter scan +++ ++
  • Sensitivity analysis +++ -
  • Visualization - +++

13/13

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