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faratian1

faratian1

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Reactions

R1

R1

E3 + HRG > E3H

R10

R10

ShcP > Shc

R11

R11

RasGDP > RasGTP

R12

R12

RasGTP > RasGDP

R13

R13

Raf > Rafa

R14

R14

Rafa > Raf

R15

R15

MEK > MEKP

R16_1

R16_1

MEKP + PP2A > MEKP_PP2A

R16_2

R16_2

MEKP_PP2A > MEK_PP2A

R16_3

R16_3

MEK_PP2A > MEK + PP2A

R17_1

R17_1

MEKP > MEKPP

R18_1

R18_1

MEKPP + PP2A > MEKPP_PP2A

R18_2

R18_2

MEKPP_PP2A > MEKP_PP2A

R18_3

R18_3

MEKP_PP2A > MEKP + PP2A

R19

R19

ERK > ERKP

R2

R2

E2 + E3H > E23H

R20

R20

ERKP > ERK

R21

R21

ERKP > ERKPP

R22

R22

ERKPP > ERKP

R23

R23

E23HP + PI3K > E23HP_PI3K

R24

R24

E23HP_PI3K > E23HP_PI3Ka

R25

R25

E23HP_PI3Ka > E23HP + PI3Ka

R26

R26

PI3Ka > PI3K

R27_1

R27_1

PI2 + PI3Ka > PI3Ka_PI

R28_1

R28_1

PIP3 + PTEN > PTEN_PIP3

R28_2

R28_2

PTEN_PIP3 > PTEN_PI

R28_3

R28_3

PTEN_PI > PI2 + PTEN

R28_4

R28_4

PTEN > PTENP

R28_5

R28_5

PTEN + PTENP > PTENP_PTEN

R28_6

R28_6

PTENP_PTEN > PTEN_PTEN

R28_7

R28_7

PTEN_PTEN > {2.0}PTEN

R29

R29

PIP3 + Akt > Akt_PIP3

R3

R3

E23H > E23HP

R30

R30

Akt_PIP3 > Akt_PI_P

R31_1

R31_1

Akt_PI_P + PP2A > Akt_PI_P_PP2A

R31_2

R31_2

Akt_PI_P_PP2A > Akt_PIP3_PP2A

R31_3

R31_3

Akt_PIP3_PP2A > Akt_PIP3 + PP2A

R32

R32

Akt_PI_P > Akt_PI_PP

R33_1

R33_1

Akt_PI_PP + PP2A > Akt_PI_PP_PP2A

R33_2

R33_2

Akt_PI_PP_PP2A > Akt_PI_P_PP2A

R33_3

R33_3

Akt_PI_P_PP2A > Akt_PI_P + PP2A

R34

R34

E23HP > ∅

R35

R35

E2 + Per > E2_Per

R36

R36

E2_Per > E2Per

R37

R37

E3H > E3H_C

R38

R38

E2 + E3H_C > E23H

R39

R39

E23H > E23H_C

R4

R4

E23HP > E23H

R40

R40

E23H_C > E23HP

R41

R41

PI3Ka_PI > PI3Ka_PIP3

R42

R42

PI3Ka_PIP3 > PI3Ka + PIP3

R43

R43

PTEN > PTEN_bpV

R44

R44

PI3K > PI3K_LY

R5

R5

E23HP + Shc > E23HP_Shc

R6

R6

E23HP_Shc > E23HP_ShcP

R7

R7

E23HP_ShcP + GS > E23HP_ShGS

R8

R8

E23HP_ShGS > E23HP + ShGS

R9

R9

ShGS > GS + ShcP

Parameters

Global parameters

Fixed species

Initial values

Functions and Rules

Assignment rules

tERKP = (ERKP + ERKPP) / tERKP_max

pAkt = (Akt_PI_PP + Akt_PI_P + Akt_PI_PP_PP2A + Akt_PI_P_PP2A) / pAkt_max

tPTENP = PTENP / 7.6

tPTEN = PTENP + PTEN + PTENP_PTEN + PTEN_PTEN + PTEN_PIP3 + PTEN_PI

tE3P = (E23HP + E23HP_PI3K + E23HP_PI3Ka + E23HP_Shc + E23HP_ShcP + E23HP_ShGS) / tE3P_max

Function definitions

Events

Constraints

Note that constraints are not enforced in simulations. It remains the responsibility of the user to verify that simulation results satisfy these constraints.


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Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of PTEN in resistance to trastuzumab.

  • Dana Faratian
  • Alexey Goltsov
  • Galina Lebedeva
  • Anatoly Sorokin
  • Stuart Moodie
  • Peter Mullen
  • Charlene Kay
  • In Hwa Um
  • Simon Langdon
  • Igor Goryanin
  • David J Harrison
Cancer Res. 2009; 69 (16): 6713-6720
Abstract
Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. Inthis study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogen-activated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies.
The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000424) Biomodels notes: The model reproduces Figure S4 of the reference publication, that correspond to the effect of heregulin-beta (black). In order to reproduce the plot that correspond to the effect of pertuzumab (blue), the initial concentration of Per should be set as 300000. For more details about the scaling factor used in the concentration of Per, look in the notes of the "Per". The data were obtained by simulation the model using Copasi v4.8 (Build 35). The plots were made using Gnuplot. JWS Online curation: This model was curated by reproducing the figures as described in the BioModels Notes. No additional changes were made.