metamodeling and modeling language for systems biology sb-uml magali roux-rouquie cnrs, paris
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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" />
http
://f
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y.fr
ee.f
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eura
lNet
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k/U
ml_
diag
ram
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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
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