CellDesigner

Akira FunahashiKeio University, Japan

Slides and Models

http://celldesigner.org/~funa/SysBio2016/

Overview

Introduction of CellDesignerWhat kind of model you can build

How to build a model with CellDesignerFrom scratchImport a model, kinetic law and parameters from existing databases

CellDesigner+ +

CellDesigner=

+

Modeling tool for biochemical and gene-regulatory network

REVIEW

A comprehensive modular map of molecularinteractions in RB/E2F pathway

Laurence Calzone1,2,3,6, Amelie Gelay1,5,6,Andrei Zinovyev1,2,3,6,*, Francois Radvanyi4,5,7

and Emmanuel Barillot1,2,3,7

1 Institut Curie, Service Bioinformatique, Paris,2 INSERM, U900, Paris, F-75248 France,3 Ecole des Mines de Paris, ParisTech, Fontainebleau, F-77300 France,4 Institut Curie, Centre de Recherche, Paris, F-75248 France and5 Institut Curie, CNRS UMR144, Paris, France6 These authors contributed equally to this work.7 These two authors are the joint senior authors of this paper.* Corresponding author: Institut Curie, Service Bioinformatique, 26 rue d’Ulm,Paris 75248, France. Tel. þ 33 1 53 10 70 50; Fax: þ 33 1 44 41 09 08;E-mail: andrei.zinovyev@curie.fr

Received 17.7.07; accepted 23.1.08

We present, here, a detailed and curated map of molecularinteractions taking place in the regulation of the cell cycle bythe retinoblastoma protein (RB/RB1). Deregulations and/ormutations in this pathway are observed in most humancancers. The map was created using Systems BiologyGraphical Notation language with the help of CellDesigner3.5 software and converted into BioPAX 2.0 pathwaydescription format. In the current state the map contains 78proteins, 176 genes, 99 protein complexes, 208 distinctchemical species and 165 chemical reactions. Overall, themap recapitulates biological facts from approximately 350publications annotated in the diagram. The network containsmore details about RB/E2F interaction network than existinglarge-scale pathway databases. Structural analysis of theinteraction network revealed a modular organization of thenetwork, which was used to elaborate a more summarized,higher-level representation of RB/E2F network. The simpli-fication of complex networks opens the road for creatingrealistic computational models of this regulatory pathway.Molecular Systems Biology 4 March 2008; doi:10.1038/msb.2008.7Subject Categories: metabolic and regulatory networks; cell cycleKeywords: cell-cycle regulation; E2F; RB pathway; RB1; systems-biology standards

This is an open-access article distributed under the terms ofthe Creative Commons Attribution Licence, which permitsdistribution and reproduction in any medium, provided theoriginal author and source are credited. Creation of derivativeworks ispermittedbut theresultingworkmaybedistributedonlyunderthesameorsimilar licencetothisone.Thislicencedoesnotpermit commercial exploitation without specific permission.

Introduction

The cell cycle is the succession of four phases called G1, S, G2andM. In dividing cells, DNA replication (S phase) andmitosis

(M phase) alternate (Alberts et al, 1994), and are separated bytwo gap phases, G1 and G2 phases. In quiescent cells, the cellsare considered to be in G0 phase. When they receive externalsignals, such as growth factors, a series of activations push thecell from a G0 to a G1 state and enters the cell cycle. The wholeprocess of cell division is mainly orchestrated by complexescomposed of two subunits, a kinase and a cyclin partner. Thesecomplexes phosphorylate a certain number of proteins, eitheractivating or inhibiting them. Among them, the retinoblasto-ma tumour suppressor protein RB (RB1) is a key regulator incell-cycle entry (transition G1/S). It sequesters a family oftranscription factors, the E2Fs, responsible for the transcrip-tion of many genes involved in cell-cycle regulation, DNAreplication and other functions like the activation of theapoptotic pathway (Muller et al, 2001). RB functions as a brakein the cell cycle, which is releasedwhen external signals triggerS-phase entry. The main targets of the external signals are theG1 cyclin/CDK complexes. Once active, the complexes, amongthem CycD1/CDK4,6, act as starters of the cell cycle (Novaket al, 2007) and phosphorylate RB, which then releases E2F(DeGregori, 2004).RB is a member of a family of proteins called the pocket

proteins (Knudsen and Wang, 1997). These proteins RB, p107and p130, share sequence similarities, especially in the ‘pocketdomain’ (Stevaux and Dyson, 2002), which is responsible fortheir repressor function. RB protein contains domains wherethe binding sites for co-repressors (E2F proteins and viraloncoproteins) are situated. These sites are subjected to mostmutations.RB is a tumour suppressor gene. Because of its implication in

so many, if not all, cancers (Sherr and McCormick, 2002), thestudy of RB regulation requires a special attention.More specifically, the RB/E2F pathway is commonly

deregulated in cancer through genetic or epigenetic mechan-isms, resulting in E2F activation. Several common oncogenes(involved in many cancer types) are the activators of thepathway, whereas several common tumour suppressor genesare inhibitors of the pathway. For example, cyclin D1 (CCND1),E2F3 and the two cyclin-dependent kinases CDK4 and CDK6can be activated by translocation, amplification or mutation,whereas RB (RB1) and the cyclin-dependent kinase inhibitorsp16INK4a (CNKN2A) and p15INK4b (CDKN2B) can beinactivated by point mutation, homozygous deletion or DNAmethylation. In addition, RB can be inactivated by severaloncogenic viral proteins including E7 from human papilloma-virus, which is responsible for more than 90% of cervicalcarcinomas (Munger et al, 2001). Tumour suppressor geneinactivation is found not only in sporadic tumours but also intumour-prone families. Germline mutations of RB1 results inretinoblastoma with a high penetrance early in youngindividuals and late in life in sarcomas and lung and bladdercarcinomas (Knudson, 1971; Nevins, 2001; Giacinti andGiordano, 2006). Germinal mutations of p16INK4a results in

& 2008 EMBO and Nature Publishing Group Molecular Systems Biology 2008 1

Molecular Systems Biology 4; Article number 173; doi:10.1038/msb.2008.7Citation: Molecular Systems Biology 4:173& 2008 EMBO and Nature Publishing Group All rights reserved 1744-4292/08www.molecularsystemsbiology.com

are connected by ‘activation’ and ‘inhibition’ relations. Theinformation about these relations is derived from the detaileddiagram. For example, in the detailed map, E2F1 is phos-

phorylated by CycA2/CDK2 and is subsequently recognizedfor degradation, which is translated in the modular map byCycA2/CDK2 module inhibiting E2F1-3 module.

Figure 2 The textbook pathway of RB has been expanded by integrating data from the literature. The E2F transcription factors (represented here by single proteins inthe nuclear compartment) are connected by activation and inhibition arrows to their gene targets. (A) Map of target genes of E2F transcription factors. Each E2Fassociates with different cofactors to activate or inhibit the transcription of many genes; pointed arrows mean activation and flat arrows mean inhibitions (B) Map ofprotein–protein interaction network. Each icon on the diagram represents distinct chemical species. See Kitano and co-workers’ description of CellDesigner’s standardnotation (Kitano et al, 2005) for a detailed meaning of shapes. When the information is available (from Atlas Oncology web-page: www.atlasgeneticsoncology.org/),tumour suppressor genes and the corresponding proteins are coloured in blue and oncogenes in red, the other proteins are in green. To read and navigate through themap, visit our webpage: http://bioinfo-out.curie.fr/projects/rbpathway/. The map is clickable and allows easy access to all included information (such as literaturereferences or standard protein ids) and hyperlinked to other databases.

A comprehensive map of RB/E2F pathwayL Calzone et al

4 Molecular Systems Biology 2008 & 2008 EMBO and Nature Publishing Group

Comprehensive pathway mapNature Molecular Systems Biology 4(173) 2008

Mathematical model

European journal of biochemistry / FEBS 267, no. 6 (2000): 1583-8.

<listOfReactions> <reaction id="re1" reversible="false" fast="false"> <listOfReactants> <speciesReference species="A"/> </listOfReactants> <listOfProducts> <speciesReference species="B"/> </listOfProducts> <kineticLaw formula=”k*A”> </kineticLaw> </reaction></listOfReactions>

CellDesigner SBMLBBiochemical

reaction A

SBML

<listOfSpecies> <species id="A" name="A" compartment="default" initialAmount="0" charge="0"/> <species id="B" name="B" compartment="default" initialAmount="0" charge="0"/></listOfSpecies>

k [A]

MathML

Over 280 software packages support SBMLhttp://sbml.org

Applications supporting SBML

Graphical NotationGraphical Notation for representing biological interactionsprotein-protein interaction, gene regulatory networks

Kitano, H. et al. "Using process diagrams for the graphical representation of biological networks", Nature Biotechnology 23(8), 961 - 966 (2005)

State transition

Process Diagram

A Visual Notation for Network Diagrams in BiologyRepresentation of Biochemical and Cellular Processes studied in Systems Biology http://sbgn.org

SBGN

Le Novère N., et al.: The Systems Biology Graphical Notation,Nature Biotechnology, 27(8), pp.735-41, (2009)

SBGN viewerCellDesigner notation SBGN PD

Nature Biotechnology, 23(8), pp.961-66, (2005)

Nature Biotechnology, 27(8), pp.735-41, (2009)

CellDesigner 4.4

http://celldesigner.org

SBML Level 2 version 4 supportGraphical notation (SBGN Viewer, SBGN-ML export)Built-in simulator (SOSlib, COPASI, Simulation Core)Database connection (BioModels.net, SABIO-RK, PANTHER, JWS Online)MIRIAM, SBO, SED-MLPlugin APIExport to PDF, PNG.Freely available

Windows (XP or later)Mac OS XLinux

Before we startPlease download & install CellDesigner 4.4 from http://celldesigner.org/

Installation Create modelCreate new model:

[File] → [New] → input title → [OK]

TipsEnable [Grid Snap] will help you draw your model much easier

Create ReactionCreate Protein “A” and “B”Draw “State transition” arrow from “A” to “B”

Create following biochemical reactionClick [Simulation] → [ControlPanel] and call SBML ODE Solver

Simulation (ex1)

k = 0.3A = 0.1B = 0

v = k[A]

Right click on the reaction and select [Edit KineticLaw...]

Simulation (ex1)

Simulation (ex1)Click [New] button on [Parameters] tab

Input values as follows:id: kname: kvalue: 0.3

k = 0.3A = 0.1B = 0

v = k[A] v = k[A]

Simulation (ex1)

Click top most text fieldType k * (k times)Select Protein “A”Click [Name] checkbox( k*s1 → k*A )

k = 0.3A = 0.1B = 0

Simulation (ex1)Double click [initialQuantity] column for Protein “A”

Set value as 0.1k = 0.3A = 0.1B = 0

v = k[A]

Simulation (ex1)Click [Simulation] → [ControlPanel]Set [End Time] to 20Click [Execute] button

Rename Species ID (s1 → A)Click [Edit] → [Replace Species ID]Click [Copy ‘Name’ into ...] buttonUse “Species Name” in KineticLaw Editor

Simulation (ex2)Create following biochemical reactionsExecute simulation from [ControlPanel]

A = 0.5

B = 0.2

k1 * A * Bk1 = 0.3

C = 0.01 D = 0.02

E = 0

F = 0

k2 * C k3 * Dk3 = 0.6k2 = 0.01

0 < t < 100

Simulation (ex2)Change parameter k1 to 30.0

A = 0.5

B = 0.2

k1 * A * Bk1 = 0.3

C = 0.01 D = 0.02

E = 0

F = 0

k2 * C k3 * Dk3 = 0.6k2 = 0.01

0 < t < 100

k1 = 30.0

k1 = 0.3 k1 = 30.0

Simulation (ex2)Click [Parameters] tabDouble click [Value] column for k1Change parameter k1 to 30.0

Simulation (ex2)Click [Interactive Simulation] tabClick [Parameter value] radio buttonClick [Define Range] buttonClick [Max] column for k1 and set value as 3.0

Drag sliderbar for k1

d[B]dt

= k1[A]� k2[B]

BA C

v1 = k1[A] v2 = k2[B]

We can reconstruct a network from ODE!

Law of Mass Action Network → ODE

d[B]dt

= k[A]

d[A]dt

= �k[A]

A CB

v1 = k[A] v2 = k[B]

d[C]dt

= k[B]

�k[B]

ODE ↔ Network

dA

dt= �k1A

dB

dt= �k2B

dC

dt= k1A + k2B � k3C

dD

dt= k3C

A

B

C

D

Michaelis-Menten kinetics

PS

E

d[P ]dt

=Vmax[S]Km + [S]

d[P ]dt

=

reac

tion

rate

SubstrateMichaelis-Menten kinetics

BA

Law of Mass action andMichaelis-Menten kinetics

v = k[A] d[A]dt

= �k[A]

d[B]dt

= k[A]

[A]

v

[A]

v

Michaelis-MentenMass-action

a

Kn + [I]n

Inhibition

PS

I

d[P ]dt

=Vmax[S]Km + [S]

�

100 1 2 3 4 5 6 7 8 9

10

0

1

2

3

4

5

6

7

8

9

[I]

d[P]/dt

a/K[I]が増加すれば抑制は強まる

双曲線

a

K + [I]

n → large

Strong inhibition with large [I]

• Mathematical modeling of biochemical reaction

Summary

• Synthesis, Decomposition• Transformation, Modification• Association, Dissociation• Catalysis• Michaelis-Menten• Inhibition• Hill equation

Circadian clock model

•Protein (P) inhibits transcription of mRNA (M)

•M is translated to Protein (R)

•P / R will be transported to cytosol / nucleus

J. theor. Biol. (2002) 216, 193–208

dM

dt=

11 + (P/h)n

� aM � sM

dR

dt= sM � (d + u)R + vP

dP

dt= uR� vP

Circadian clock model

0.1

0.5 0.5

0.5

Circadian clock model

0.1

0.5

0.5

0.5

1 / (1+ pow(P/h, n))

uR dR

vP

sM

aM

a = s = d = v = 1.0u = 0.1h = 0.01n = 40

dM

dt=

11 + (P/h)n

� aM � sM

dR

dt= sM � (d + u)R + vP

dP

dt= uR� vP End Time: 50

Num. of Points: 1,000

xn = pow(x, n)

Boundary condition Circadian clock model

mRNA

R

dM

dt=

11 + (P/h)n

� aM � sM

dR

dt= sM � (d + u)R + vP

dP

dt= uR� vP

n = 40

Oscillation(stable limit cycle)

Qualitative change by ‘n’

End Time: 50Num. of Points: 1,000

dM

dt=

11 + (P/h)n

� aM � sM

dR

dt= sM � (d + u)R + vP

dP

dt= uR� vP

n = 8

Stable fixed point

Qualitative change by ‘n’

End Time: 50Num. of Points: 1,000

Why we simulate a model?

n = 40 n = 8 Stable fixed pointOscillation(stable limit cycle)

Mathematical model and Quantitative evaluation (Simulation) will reach a Qualitative conclusion

SummaryIntroduction of CellDesigner

What kind of model you can buildMathematical modelPathway map

How to build a model with CellDesignerFrom scratchImport a model from BioModels.netImport kinetic law and parameters from SABIO-RK

AcknowledgementSBML * SBML communitySBGN * SBGN community

SBW * Frank Bergmann * Herbert Sauro

SOSlib * Rainer Machne * Christoph Flamm

Simulation Core (Univ. of Tuebingen) * Andreas Dräger

COPASI (Univ. of Heidelberg) * Ralph Gauges * Sven Sahle * Ursula Kummer

SABIO-RK (HITS gGmbH) * Martin Golebiewski * Lenneke Jong * Wolfgang Mueller

* Isabel Rojas

CellDesigner (SBI) * Yukiko Matsuoka * Samik Ghosh * Hiroaki Kitano (Keio Univ.) * Noriko Hiroi * Kazushige Nakamura * Kei Sumiyoshi * Kota Mashimo * Kaito Ii * Akiya Jouraku (NIAS) * Norihiro Kikuchi (MKI)

AcknowledgementGrant-in-Aid for Young Scientists (21700328)Grant-in-Aid for Scientific Research on Innovative Areas (23136513)Grant-in-Aid for Scientific Research (24300112)ERATO Kawaoka Infection-Induced Host Response Project