A logic modelling workflow for systems pharmacology Luis Tobalina - - PowerPoint PPT Presentation


 Institute for Computational Biomedicine 
 Heidelberg University & RWTH Aachen

www.saezlab.org @sysbiomed

Luis Tobalina

A logic modelling workflow for systems pharmacology

13/07/2018

Outline

• Context
• Pipeline
• Network
• Modelling
• Data
• Insights
• Summary

2

Systems Biomedicine

• Systems Biology examines how cell components

interact and form networks and how the networks generate whole cell functions corresponding to

• bservable phenotypes (Palsson, 2006)
• Systems Biomedicine addresses the challenge of

translating insights in biological systems to clinical application

3

Systems Pharmacology

• Systems Pharmacology is the application of the

concepts of systems biomedicine to pharmacology in

• rder to understand the full effect of a drug
• Personalised medicine aims to match each patient with

their most beneficial treatment

4

Disease and Therapy

• Identifying key drivers of a disease helps us to design

targeted therapies

• Drug combinations may help in diseases driven by

several altered proteins

• In diseases like cancer, we also have to address

development of resistance to the applied therapy

• Not all the targets are actionable
• Not all the therapies need to target the diseased cell:

immunotherapy

5

Importance of signalling networks

• Melanoma patients with BRAF mutation show

response to BRAF inhibitors

6

BRAF Proliferation Vemurafenib

Note: simplified interaction diagram

Importance of signalling networks

• But resistance to treatment eventually develops,

leading to relapse

7

Wagle et al., J Clin Inv, 29:22, 2011

Figure 2 in Wagle et al. (2011) shows BRAF-mutant melanoma patient (A) before treatment, (B) after 15 weeks

• f therapy, and (C) after relapse, after 23 weeks of therapy.

Importance of signalling networks

• Colon cancer patients with the same mutation show

resistance to treatment because of EGFR feedback loop

8

EGFR PI3K AKT BRAF Proliferation Vemurafenib

Note: simplified interaction diagram

Challenges

• Biomedical research faces different challenges:
• Noise
• Batch effects
• Small sample size
• Difficult / Expensive experiments
• Possible ways of dealing with these:
• Well thought and designed experiments
• Pool information from different studies
• Use of prior knowledge
• Development of mathematical models

9

f(x)

Pipeline overview

10

Question PKN Logic Model Fitted Model Literature Data Optimizer Modeling formalism Data Bases Analysis Experimental data Literature Visualization Network analysis Simulation In-silico perturbations

compare

Raw Data

normalize

Structural Analysis Prediction

f(x)

CellNOpt MaBoSS Cytoscape Omnipath

Traynard et al. CPT: Pharmacometrics & systems pharmacology 2017

Biological question

• The first step of modelling is to start with a biological

question of interest

• Example: what are the changes in the

phosphoproteomic response to the PI3K pathway when a prostate cancer cell goes from being castration sensitive to castration resistant

11

Question

Network model

12

Question PKN Literature Data Bases

Omnipath

Türei et al. Nature Methods 2016

Cell cycle MYC Caspase9 Caspase8 Survival

AND AND OR OR AND

TNFR p53 IKK p38 NFkB

AND AND

OmniPath

• http://omnipathdb.org

13

graphics by Spencer Phillips

Türei, Korcsmáros & Saez-Rodriguez (2016). Nat Methods, 13(12)966-967.

OmniPath

• OmniPath is a comprehensive collection of literature

curated human signaling pathways

• Why Omnipath?
• Available via a webservice or using pypath, a Python

module for molecular networks and pathways analysis

Signor DeathDomain DEPOD ARN ELM dbPTM PhosphoSite NRF2ome SPIKE phosphoELM Guide2Pharma CA1 SignaLink3 DOMINO Macrophage LMPID HPRD-phos PDZBase 50 100 500 1000 2000 0.05 0.10 0.20 0.50

similarity over interactions number of interactions Türei, Korcsmáros & Saez-Rodriguez (2016)

14

http://omnipathdb.org/

15

OmniPath

15 Türei, Korcsmáros & Saez-Rodriguez (2016)

Network model

16

HSP27 Stress Rac p38 EGFR Survival RAS Caspase9 ERK1_2 EGF MEK IGF1_R mTOR RPS6 AKT PI3K IGF_1 IL6 Cell cycle IL6R beta catenin MYC Jak GSK3 AR Stat3 DHT TNFR IKK NFkB Caspase8 p53 JNK TNFa measured inhibited & measured inhibited stimulated

Experimental conditions:

Traynard et al. CPT: Pharmacometrics & systems pharmacology 2017

Logic model

17

Question PKN Logic Model Literature Modeling formalism Data Bases Literature

f(x)

Omnipath

Choice of modelling formalism

The amount of details to include in the model and the mathema&cal formalism used to describe the process should be lead by the biological ques&on (and by available data).

Figures from: Saez-Rodriguez J, et al. Annual Rev Biomed Eng, 2015

Physicochemical modeling Causal (logic) modelling

18

Variety of formalisms

• Boolean simulation with synchronous updates
• Constrained fuzzy logic
• Simulations with multiple time-scales
• Logic based ODEs
• …

19

Using logic ODE as modelling formalism

Based on ordinary differential equations derived from logic models using a continuous update function

xi1 xi2 xiN xi

…

DM Wittmann, et al., 2009, BMC Systems Biology F Eduati, 2017, Cancer Research

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0

x

f(x)

f(x) = 1 −

(1− x)n (1− x)n+kn 1 1+kn

0 ≤ k ≤ 1, n = 3

k=0 k=1

˙ xi = τi(Bi(f(xi1), f(xi2), . . . , f(xiN)) − xi) dxi dt

dxB dt = τB[f(xA) − xB]

A B

20

Using logic ODE as modelling formalism

τi

kij

is the life-time of species i =0 node not functional >0 higher functionality strength of regulation j → i =0 no edge >0 stronger interaction x1 1 1 x2 1 1 B(f(x1), f(x2)) = ... 0(1 − f(x1))(1 − f(x2))+ (1 − f(x1))f(x2)+ f(x1)(1 − f(x2))+ f(x1)f(x2)

Generalisation for OR gates

• Easily interpretable parameters
• Direct derivation from logic rules

21

Collect data

• Objective: obtain data for training logic models
• Priority: high number of perturbations

22

Question PKN Logic Model Fitted Model Literature Data Optimizer Modeling formalism Data Bases Literature Raw Data

normalize

f(x)

CellNOpt Omnipath

Phospho-proteomics to look at signal transduction

An&body-based methods: low coverage many condiIons

1 10 100 1000 1 10 100 1000

Reverse Phase Protein Arrays Western Blot MicroWestern Intracellular Flow Cytometry T argeted MS Protein Microarrays xMAP Label Free MS Labeled MS

T arget proteins Samples

Terfve C, Saez-Rodriguez J, Adv. Syst. Biol., 2012 Saez-Rodriguez J, et al. Annual Rev Biomed Eng, 2015

Mass-spectrometry methods: high coverage few condiIons

23

Phospho-proteomics

24

https://pharmchem.ucsf.edu/research/physbio/proteomics

Credit: By Philippe Hupé [CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

Image showing mass-spectrometry protocol (https:// upload.wikimedia.org/wikipedia/commons/1/1f/ Mass_spectrometry_protocol.png)

Preparing the data

• Normalisation challenges
• Boolean logic works with binary values, but

measurements are continuous values

• CellNOpt ODE works with values between 0 and 1
• Coverage challenges
• Not all the nodes in the model may be covered by the

measurements

• Use of derived measurements
• e.g. Kinase activities

25

Use data for training models

26

Question PKN Logic Model Fitted Model Data Optimizer Raw Data

normalize

CellNOpt

Experiments Literature Data Bases

Omnipath

Modeling formalism

f(x)

www.cellnopt.org

27

Broad spectrum of modelling formalism
 with different level of detail

CellNet Optimizer Computable Model specific to data

(cell/time/conditions)

Networks

New sources Boolean -- -- quantitative 
 steady-state -- -- time series

t

1

t

+ detail , - scope 1

y n

CellNOptR 2t CellNOptR CNORfuzzy CNORode CNORdt

y y n y n

2 t? >2 t rich data? ~small network? scarce data? large network? partial efgects negligible? time course data?

Terfve C Cokelaer T 
 MacNamara A Henriques D 
 Gonçalves E Morris MK 
 van Iersel M Lauffenburger DA 
 Saez-Rodriguez J 
 BMC Syst Biol, 6:133, 2012

28

• PHONEMeS (PHOsphorylation NEtworks for Mass

Spectrometry) is a method to model signalling networks based on untargeted phosphoproteomics mass spectrometry data and kinase/phosphatase- substrate interactions (Terfve et al. 2015 Nature communications)

• We can use it to combine high-throughput data

(SWATH phospho-proteomics) with a large scale background network (e.g. Omnipath)

PHONEMeS

29

PHONEMeS

30

LNCaP LNCaP-ablated

target kinase intermediate kinase measured phosphorite

Fitting example

31

Question PKN Logic Model Fitted Model Literature Data Optimizer Modeling formalism Data Bases Literature Raw Data

normalize

f(x)

CellNOpt Omnipath

Assessing fitted networks

32

Stat3 Jak beta catenin IL6 GSK3 IL6R Cell_cycle DHT IGF1_R IGF_1 MYC AKT AR mTOR PI3K RPS6 p53 Caspase8 NFkB IKK TNFR TNFa JNK MEK RAS HSP27 p38 EGFR Caspase9 Stress Rac Survival EGF ERK1_2

1 0.04 0.001

node life-time (parameters τ)

1 0.66 0.33

edge strength (parameters k) compressed

0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00

normalised measured values model simulation

C

Pearson Correlation = 0.66 (p-value<2.2*10-16) MSE = RSS/N = 0.017

A B

−0.25 −0.25

Traynard et al. CPT: Pharmacometrics & systems pharmacology 2017

Assessing fitted networks

33

B

JNK GSK3 RPS6 AKT p38 Stat3 ERK1_2 HSP27

0.05 0.1 0.15 0.2 0.25

p38 i mTOR i MEK i IKK i PI3K i DHT TNFa IL6 IGF_1 EGF

Fitting error

Experimental condition Measured Phosphoproteins Mean Squared Error (MSE) Stimulus Inhibitor 30 240 30 240 30 240 30 240 0.5 1

measured data model simulation

activity time (min)

−0.25 −0.25

Traynard et al. CPT: Pharmacometrics & systems pharmacology 2017

How to deal with incomplete prior knowledge?

34

New sources

Federica Edua&

CNOFeed: Link CellNOpt to methods 
 to infer new links


A E Gi C F H 1 1 1 0.5 0.1 0.1 1 1 0.4 0.7 0.6 1 1 0.4 0.5 0.6

A.Compression D. Integration

Prior Knowledge Network (PKN) Compressed Network Data-driven Network (DDN) Integrated Network Trained Model

B. Training

Data

E. Weighting

Protein Interaction Network (PIN)

CellNOptR CNORFeeder

C. Inference

AND

Eduati F, de las Rivas J, di Camilo B, Toffolo G, Saez-Rodriguez J 
 Bioinformatics 10.1093/bts363, 2012

Assessing fitted networks

35

Mean Squared Error (MSE) 0.020 0.025 0.030 0.035 best model

bootstrap randomized network randomized data

Coefficient of Determination (COD) −0.25 0.00 0.25 best model

bootstrap randomized network randomized data

Pearson Correlation (r) −0.25 0.00 0.25 0.50

bootstrap randomized network randomized data

best model

D

Traynard et al. CPT: Pharmacometrics & systems pharmacology 2017

Further analysis

36

Question PKN Logic Model Fitted Model Literature Data Optimizer Modeling formalism Data Bases Analysis Literature Raw Data

normalize

f(x)

CellNOpt MaBoSS Omnipath

MaBoSS

• MaBoSS is a C++ software for simulating continuous/

discrete time Markov processes, applied on a Boolean network

• Given some initial conditions, MaBoSS applies Monte-

Carlo kinetic algorithm (or Gillespie algorithm) to the network to produce time trajectories. Time evolution of probabilities are estimated

37

https://maboss.curie.fr

MaBoSS simulations

38

A

Cell_cycle−Caspase9−MYC−Survival Cell_cycle−Caspase8−Caspase9−MYC Cell_cycle−MYC−Survival Cell_cycle−Caspase8−MYC−Survival Cell_cycle−Caspase9 null Caspase8−MYC

. . 1 . 2 . 3 . 4

Probability States B

0.00 0.25 0.50 0.75 1.00

3000 6000 9000

Time units Probability

Survival WT Survival iPI3K Survival imTOR

• 0.00

0.25 0.50 0.75 1.00

AKT AR beta_catenin Caspase8 Caspase9 EGFR ERK1_2 GSK HSP27 IGF1_R IKK IL6R Jak JNK MEK mTOR MYC NFkB p38 p53 PI3K Rac RAS RPS6 Stat3 TNFR

Inhibited node Survival probability Inhibition

• 0.1

0.5 1

Jak Caspase9 p53 NFkB p53 IKKa TNFR Rac PI3K MEK AKT RPS6 RAS ERK1_2 RAS mTOR Caspase9 AKT Caspase8

Traynard et al. CPT: Pharmacometrics & systems pharmacology 2017

Further analysis

39

Question PKN Logic Model Fitted Model Literature Data Optimizer Modeling formalism Data Bases Analysis Literature Raw Data

normalize

Structural Analysis

f(x)

CellNOpt MaBoSS Cytoscape Omnipath

Example in colorectal cancer

• Can we use logic models of signalling networks to

understand and target drug resistance?

40

...

A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5

...

Dynamic logic models provide 
 mechanistic insight and novel biomarkers

41 Generic Signaling Network

Phosphoproteomic data (perturbations)

CRC cell line 1 CRC cell line 2 CRC cell line 14

EduaI et al. Cancer Res, 2017

• w. N.

Bluethgen & M. Garnett

Colorectal (CRC) cell lines from GDSC screening

...

A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5

...

1 N 2

cell survival drug concentration

CRC cell line 1 CRC cell line 2 CRC cell line 14

cell survival drug concentration cell survival drug concentration

...

IC50 IC50 IC50

...

A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5

...

Dynamic logic models provide 
 mechanistic insight and novel biomarkers

41 Generic Signaling Network

Phosphoproteomic data (perturbations) Drug sensitivity data (GDSC)

CRC cell line 1 CRC cell line 2 CRC cell line 14

EduaI et al. Cancer Res, 2017

• w. N.

Bluethgen & M. Garnett

Colorectal (CRC) cell lines from GDSC screening

...

A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5

...

1 N 2

cell survival drug concentration

CRC cell line 1 CRC cell line 2 CRC cell line 14

cell survival drug concentration cell survival drug concentration

...

IC50 IC50 IC50

...

A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5

...

Dynamic logic models provide 
 mechanistic insight and novel biomarkers

41

Prior knowledge network (curated from public resources) Cell line specific models 14 colorectal cancer (CRC) cell lines Associations between model parameters and drug response (sensitivity - IC50) Phosphoprotemics data under perturbed conditions (42 perturbations,14 readouts)

parameter 1 drug 1 parameter 2 drug 1 parameter M drug 1

...

parameter 1 drug 2 parameter 2 drug 2 parameter M drug 2

...

parameter 1 drug N parameter 2 drug N parameter M drug N

... ... ... ...

1 N 2

cell survival drug concentration

CRC cell line 1 CRC cell line 2 CRC cell line 14

cell survival drug concentration cell survival drug concentration

...

IC50 IC50 IC50

...

CRC cell line 1 CRC cell line 2 CRC cell line 14

A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5 A1 A2 I1 I2 1 1 1 1 1 M1 M2 M3 M4 M5

Drug response (sensitivity) data Cell line specific models (using logic ODEs and L1 regularisation)

...

Generic Signaling Network

Phosphoproteomic data (perturbations) Drug sensitivity data (GDSC) Associations model parameters - drug sensitivity

CRC cell line 1 CRC cell line 2 CRC cell line 14

EduaI et al. Cancer Res, 2017

• w. N.

Bluethgen & M. Garnett

Colorectal (CRC) cell lines from GDSC screening

CRC Cell lines specific models

42

HT29 HT115 SKCO1 SNUC2B SNUC5 HCT116 DIFI COLO320HSR CCK81 CAR1 SW1116 SW1463 SW620 SW837

EduaI et al. Cancer Res, 2017

Model-based biomarkers 


• f drug efficacy and resistance

43

kERK, RSKp90 τERK Afatinib (EGFR, ERBB2) τIkBa kERK, RPS6 SB590885 (BRAF) kIKK, ERK kTGFRb, SMAD2 τGSK3 RDEA119 (MEK1, MEK2) kIKK, ERK kMEK, ERK HG-5-88-01 (EGFR) τGSK3 kRSKp90, GSK3 kIKK, ERK AZD6244 (MEK1, MEK2) kIKK, ERK PD-0325901 (MEK1, MEK2) kERK, RSKp90 Dabrafenib (BRAF) kIKK, TBK1 kTGFRb, SMAD2 kMP2K4, JNK (5Z)-7-Oxozeaenol (TAK1) EGF TNFa IGF1 TGFb HGF SMAD2 TAK1 IRS1 PI3K EGFR TGFRb RASK RSKp90 S6K ERK RPS6 p38 IkBa PAK1 JNK AKT BRAF M3K1 cJun MP2K4 GSK3 mTOR TBK1 IKK MEK

No genetic biomarker

EduaI et al. Cancer Res, 2017

Model-based biomarkers 


• f drug efficacy and resistance

43

kERK, RSKp90 τERK Afatinib (EGFR, ERBB2) τIkBa kERK, RPS6 SB590885 (BRAF) kIKK, ERK kTGFRb, SMAD2 τGSK3 RDEA119 (MEK1, MEK2) kIKK, ERK kMEK, ERK HG-5-88-01 (EGFR) τGSK3 kRSKp90, GSK3 kIKK, ERK AZD6244 (MEK1, MEK2) kIKK, ERK PD-0325901 (MEK1, MEK2) kERK, RSKp90 Dabrafenib (BRAF) kIKK, TBK1 kTGFRb, SMAD2 kMP2K4, JNK (5Z)-7-Oxozeaenol (TAK1) EGF TNFa IGF1 TGFb HGF SMAD2 TAK1 IRS1 PI3K EGFR TGFRb RASK RSKp90 S6K ERK RPS6 p38 IkBa PAK1 JNK AKT BRAF M3K1 cJun MP2K4 GSK3 mTOR TBK1 IKK MEK

No genetic biomarker

EduaI et al. Cancer Res, 2017

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter

Identified and validated novel biomarkers 
 and a new combination strategy

44

EduaI et al. Cancer Res, 2017

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter

Identified and validated novel biomarkers 
 and a new combination strategy

44

?

EduaI et al. Cancer Res, 2017

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter 40 100

GSK3 i

HT29

combo MEK inhibitor + increasing GSK3 inhibitor

SB216763

GSK3 inhibitors

CHIR-99021

Identified and validated novel biomarkers 
 and a new combination strategy

44

no improved sensitivity when GSK3 is not functional

?

EduaI et al. Cancer Res, 2017

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter 40 100

GSK3 i

HT29

combo MEK inhibitor + increasing GSK3 inhibitor

SB216763

GSK3 inhibitors

CHIR-99021

DIFI CCK81 COLO320HSR HCT116 HT115 HT29 SKCO1 SNUC2B SNUC5 SW1116 SW1463 SW620 SW837 CAR1

• 2

2 4 0.00 0.05 0.10 0.15 0.20 0.25

Pearson Correlation = 0.66 p-value=0.01

MEK inhibitor

drug sensitivity (log IC50)

GSK3 parameter (τGSK3)

pathway model parameter 40 100

GSK3 i

HT29

100 40

GSK3 i

CCK81

combo MEK inhibitor + increasing GSK3 inhibitor

SB216763

GSK3 inhibitors

CHIR-99021

combo MEK inhibitor + increasing GSK3 inhibitor

SB216763

GSK3 inhibitors

CHIR-99021

Identified and validated novel biomarkers 
 and a new combination strategy

44

no improved sensitivity when GSK3 is not functional synergistic combo when GSK3 is functional

?

EduaI et al. Cancer Res, 2017

Summary

45

Question PKN Logic Model Fitted Model Literature Data Optimizer Modeling formalism Data Bases Analysis Experimental data Literature Visualization Network analysis Simulation In-silico perturbations

compare

Raw Data

normalize

Structural Analysis Prediction

f(x)

CellNOpt MaBoSS Cytoscape Omnipath

Traynard et al. CPT: Pharmacometrics & systems pharmacology 2017

46

Saez-Rodriguez group Special thanks to: Collaborators: Funding: @sysbiomed www.saezlab.org

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668858.

Julio Saez-Rodriguez Pauline Traynard Laurence Calzone Federica Eduati Attila Gabor


 Institute for Computational Biomedicine 
 Heidelberg University & RWTH Aachen

www.saezlab.org @sysbiomed

Luis Tobalina

A logic modelling workflow for systems pharmacology

13/07/2018