Metamodeling and Modeling language for Systems Biology

SB-UML

Magali ROUX-ROUQUIE

CNRS, Paris

What is Systems Biology ?

T. Ideker, T. Galitski, L. Hood. A new approach to decoding life:Systems biology. Annu. Rev. Genomics Hum Genet 2 (2001) 343-372.

DATAlevel 1

DATA MODELlevel 2

Virtual Cell

Relational ModelRelational Model Object ModelObject Model SBML (XML) fileSBML (XML) file OntologyOntology

<math xmlns="http://www.w3.org/1998/Math/MathML"> <apply id="membrane_voltage_diff_eq"><eq /> <apply><diff /> <bvar><ci> time </ci></bvar> <ci> V </ci> </apply> <apply><divide /> <apply><minus /> <apply><plus /> <ci> i_Na </ci> <ci> i_K </ci> <ci> i_L </ci> </apply> </apply> <ci> C </ci> </apply> </apply> </math> </component>

<component name="sodium_channel"> <!-- the following variables are used in other components --> <variable name="i_Na" public_interface="out" units="microA_per_cm2" />

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DATAlevel 1

DATA MODELlevel 2

METAMODELlevel 3

METAMETAMODEL

level 4

Virtual Data

Relational ModelRelational Model Object ModelObject Model SBML (XML) fileSBML (XML) file OntologyOntology

<math xmlns="http://www.w3.org/1998/Math/MathML"> <apply id="membrane_voltage_diff_eq"><eq /> <apply><diff /> <bvar><ci> time </ci></bvar> <ci> V </ci> </apply> <apply><divide /> <apply><minus /> <apply><plus /> <ci> i_Na </ci> <ci> i_K </ci> <ci> i_L </ci> </apply> </apply> <ci> C </ci> </apply> </apply> </math> </component>

<component name="sodium_channel"> <!-- the following variables are used in other components --> <variable name="i_Na" public_interface="out" units="microA_per_cm2" />

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Languagemodeling language

abstract syntaxconcrete syntax(graphical notation)

semantics

semantic domain semantic mappingsyntax mapping

METAMODELMETAMODELlevellevel

MODELlevel

BIND database model Action ontology model

?Metamodel

forSystems Biology

Looking for a Conceptual Metamodel

…

Static

Dynamic

Mapping between State Description and Process Description

Internal

Environment

Behavior

GS

1. Ideogram of the General System

Form

Space

Time

State (A)

State(B)

Transition

Event

2. Ideogram of the Process

3. Process-Oriented Modeling

FunctionExternal

Environment

M. Roux-Rouquie and JL Le Moigne. The systemic paradigm and its relevance for modeling biological functions (2002) CR Biol. 325: 419-430.

Looking for a modeling language:The Unified Modeling Language (1)

UML is a language originally developed for software engineering.This is a language for visualizing, specifying, constructing and documenting any kind of systems. It decouples the model from the platform.It describes:

–concepts and their relationships (abstract syntax and semantics)

–constraint language (OCL)

–diagrams and notation for concepts

–interchange format (XMI)

Major Advantages

•A standard language maintained by OMG and used by

thousands of people over the world,

•Both, the formalism and the knowledge can be

represented in UML

•Different formalisms can be expressed in the same

standardized language

Looking for a modeling language:The Unified Modeling Language (2)

The Unified Modeling Language (UML)

State diagram Interaction diagram

UML State diagram and Interaction Diagram

Extensions for RT systems with UML 2.0 (IBM Rational)

Passive class Active class

A SB profile with UML 2.0(CNRS proposal against RPF14)

OMG Life Sciences Research DTF Meeting

Boston, September 9th 2003

SB-UML: FormOccurrence

C_Organism C_Tissue C_Cell C_Organelle C_MolecularC_OrganismC_Organism C_TissueC_Tissue C_CellC_Cell C_OrganelleC_Organelle C_MolecularC_Molecular

C_CellEpithelial C_CellLymphocyteC_CellEpithelialC_CellEpithelial C_CellLymphocyteC_CellLymphocyte

C_Nucleus C_RibosomeC_NucleusC_Nucleus C_RibosomeC_Ribosome

C_RNAC_RNA C_DNAElementC_DNAElement C_ProteinC_Protein

C_PromoterC_Promoter C_RegulatoryElementC_RegulatoryElement C_GeneC_Gene

BioComponentBioComponent

1..*1..* 1..*1..* 1..*1..*

BioTransformationBioTransformation

TR_Molecular TR_CellularTR_MolecularTR_Molecular TR_CellularTR_Cellular

TR_MolecularCovalent TR_MolecularNonCovalentTR_MolecularCovalentTR_MolecularCovalent TR_MolecularNonCovalentTR_MolecularNonCovalent

TR_CovalentProtein TR_CovalentRNA TR_CovalentDNATR_CovalentProteinTR_CovalentProtein TR_CovalentRNATR_CovalentRNA TR_CovalentDNATR_CovalentDNA © C

NR

S

FormOccurrence

BioTransformationBioComponent

1..* *

Example: TR_Photo-Isomerization

• Definition: Modifications of the molecular geometry after the absorption of a photon.

Molecular State vs population

• Modeling

• Simulation

CellML vs SB-UML <variable name="C" initial_value="1.0" units="microF_per_cm2" /> <math xmlns="http://www.w3.org/1998/Math/MathML"> <apply id="membrane_voltage_diff_eq"><eq /> <apply><diff /> <bvar><ci> time </ci></bvar> <ci> V </ci> </apply> <apply><divide /> <apply><minus /> <apply><plus /> <ci> i_Na </ci> <ci> i_K </ci> <ci> i_L </ci> </apply> </apply> <ci> C </ci> </apply> </apply> </math>

Modeling States of cyclin-dependent Cdk2 kinase

/ cycA : CycA / cdk2 : Cdk2

+ / Binding1 : com plexation

+ / Binding1 : com plexation~

+ / Binding2 : com plexation~

+ / KinaseActivty : Phosphorylation~

+ / Binding2 : com plexation~ + / destroyCom plex

: Phosphorylation

+ / KinaseActivity : Phosphorylation~

+ / Cdk2_P : Phosphorylation

/ cycA : CycA / cdk2 : Cdk2

+ / Binding1 : com plexation

+ / Binding1 : com plexation~

+ / Binding2 : com plexation~

+ / KinaseActivty : Phosphorylation~

+ / Binding2 : com plexation~ + / destroyCom plex

: Phosphorylation

+ / KinaseActivity : Phosphorylation~

+ / Cdk2_P : Phosphorylation

CycA_Cdk2

<<SpaceO ccurrence>> N ew Attribute2<<Tim eO ccurrence>> N ew Attribute3<<Form O ccurrence>> N ew Attribute1

N ew O peration1()

+ / Binding2 : com plexation~+ / destroyCom plex : Phosphorylation+ / KinaseActivity : Phosphorylation~

<<Capsule::FU nProteinCom plex>>

CycA

+ / Binding1 : com plexation~

<<Capsule::FU nProtein>>Cdk2

+ / Binding1 : com plexation+ / Binding2 : com plexation~+ / KinaseActivty : Phosphorylation~+ / Cdk2_P : Phosphorylation

<<Capsule::FU nProtein>>

/ cycA / cycA / cdk2 / cdk2

Individual cell vs population

Signal (molecule concentration) per cell

In a imited population (less than 10 cells)

In a large population

Conclusion (1)

• Model-driven approach

• Metamodel for systems biology in UML

• UML models in a variety of formalisms

• Semantic integration (before 2010, as announced by Alex Polonsky !)

Conclusion (2)• Model transformation

TargetMetamodel

SourceMetamodel

SourceModel

TargetModel

Transf ormation Engine

Transf ormation Rules

I nstance of I nstance of