Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration for data and are often used to annotate biosimulation models in systems biology. Here, I present an approach towards combining both disciplines in a common framework that enables information to flow between both.

1. Towards integration of systems biology and biomedical
ontologies
Robert Hoehndorf
Department of Genetics
University of Cambridge
29 March 2011
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2. Introduction Motivation
Motivation
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3. Introduction Motivation
Motivation
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4. Introduction Ontology
Applied ontology
ontology (philosophy) studies the nature of existence and categories
of being
an ontology (computer science) is the “explicit specification of a
conceptualization of a domain” [Gruber, 1993]
ontologies specify the meaning of terms in a vocabulary
formalized ontologies can be used by computers and automated
systems
Applied ontology is the branch of knowledge representation that focuses
on the content.
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5. Introduction Ontology
Open Biomedical Ontologies (OBO)
Individual
Physical object Quality Function Process
ChEBI Ontology Molecule
Gene
Sequence Ontology
Transcript
GO-CC Organelle
Celltype Gene Ontology Cell
Phenotype Tissue
Ontology Organ
Anatomy
Ontology
Body
Population
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6. Introduction Ontology
Systems biology
Systems biology...is about putting together rather than taking
apart, integration rather than reduction. [Denis Noble]
multi-scale data integration
domains and levels of granularity
species
kinds of data
integration of in silico, in vitro and in vivo research
focus on emergent properties
simulation of biological systems
predict and simulate systems’ behavior
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7. Introduction Ontology
Systems biology
Challenges (Kitano, 2002)
data integration
validation
standard languages
specification
exchange
results
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8. Introduction Ontology
Systems biology
Challenges (Kitano, 2002)
data integration
validation
standard languages
specification
exchange
results
Can we use ontologies to address these problems?
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9. Harvesting SBML
MIRIAM annotations
Annotation of SBML
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10. Harvesting SBML
MIRIAM annotations
Annotation of SBML
MIRIAM provides annotation of SBML entities
ontologies are treated as meta-data
search
semantic similarity
documentation
no integration with modelling language
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11. Harvesting SBML
MIRIAM annotations
Information flow hypothesis
Integration of SBML and ontologies could lead to information flow
between models and ontologies.
Information flow enables the use of ontologies for
verification,
access to data,
integration and combination of models.
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12. Harvesting SBML
MIRIAM annotations
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13. Harvesting SBML
Ontological commitment
Rule 1: models
Model M annotated with A1:
M represents an object O1
O1 can have functions
O1 ’s functions can be realized by processes
model components represent parts of O1
M SubClassOf: represents some A1
M SubClassOf: represents some (has-function some A1)
M SubClassOf: represents some (has-function some
(realized-by only A1)
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14. Harvesting SBML
Ontological commitment
BioModel 82
annotated with heterotrimeric G-protein complex cycle (GO:0031684):
represents an object O1
O1 has a function F1
F1 is realized by processes of the type heterotrimeric G-protein
complex cycle
M SubClassOf: represents some O1
O1 SubClassOf: (has-function some (realized-by only
GO:0031684)
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15. Harvesting SBML
Ontological commitment
Rule 2: Compartments
Compartment C annotated with A2:
represents an object O2
part of the O1
compartment’s species represent objects that are located in O2
C SubClassOf: represents some A2
A2 SubClassOf: located-in some A1
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16. Harvesting SBML
Ontological commitment
Compartment “Cell” in BioModel 82
annotated with Cell (GO:0005623):
represents an object O2
O2 is a kind of Cell
O2 is part-of O1
C SubClassOf: represents some O2
O2 SubClassOf: Cell and part-of some O1
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17. Harvesting SBML
Ontological commitment
Compartment “Cell” in BioModel 82
annotated with Cell (GO:0005623):
represents an object O2
O2 is a kind of Cell
O2 is part-of O1
C SubClassOf: represents some O2
O2 SubClassOf: Cell and part-of some O1
O2 SubClassOf: Cell and part-of some (has-function
some (realized-by only GO:0031684))
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18. Harvesting SBML
Ontological commitment
Rule 3: Species
represents an object O3
O3 can have functions
O3 ’s functions can be realized by processes
O3 can have qualities (concentration, amount, charge,...)
located in O2
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19. Harvesting SBML
Ontological commitment
Species GTP in “Cell” in BioModel 82
annotated with GTP (CHEBI:15996):
represents an object O3
O3 is a kind of GTP
O3 is located-in O2
S SubClassOf: represents some O3
O3 SubClassOf: GTP and located-in some O2
O3 SubClassOf: GTP and located-in some (Cell and
part-of some (has-function some (realized-by only
GO:0031684)))
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20. Harvesting SBML
Ontological commitment
Reaction
represents an object O3 with a function F
F is realized by P
P has participants (inputs, outputs and modifiers) O4
O4 are objects represented by species
P occurs in O1
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21. Harvesting SBML
Ontological commitment
Reaction GTP-binding in BioModel 82
annotated with GTP binding (GO:0005525):
represents an object O4
O4 has a function F4
F4 is a kind of GTP binding
F4 is realized by P4
P4 has-input O3 (GTP)
R SubClassOf: represents some (has-function some F4)
F4 SubClassOf: GTP binding and realized-by only P
P SubClassOf: has-input some O3
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22. Harvesting SBML
Ontological commitment
Reaction GTP-binding in BioModel 82
World of BIOMD0000000082
BIOMD0000000082 - Thomsen1988 AdenylateCyclase Inhibition
has-function (realized-by)
heterotrimeric G-protein complex cycle
Cell in
Compartment "cell" represents World of BIOMD0000000082
has-part Cell
part-of
DRG GDP GTP has-part GTP binding in world of
World of BIOMD0000000082
BIOMD0000000082
has-part GTP
represents GTP
part-of Cell in
World of
Reactions BIOMD0000000082
Reaction: GTP binding with DRG represents
GTP binding in world of
BIOMD0000000082
represents* has-input
Parameter
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23. Harvesting SBML
Ontological commitment
BioModels Result
Ontologies:
FMA
ChEBI
GO
Celltype
PATO
(KEGG, Reactome)
Result on BioModels:
more than 300,000 classes
more than 800,000 axioms
90,000 complex model annotations
http://sbmlharvester.googlecode.com
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24. Harvesting SBML
Inconsistency
Compartments/species annotated with functions or processes
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25. Harvesting SBML
Inconsistency
Biological inconsistency: Biomodel 176
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26. Harvesting SBML
Inconsistency
Biological inconsistency: Biomodel 176
[Term]
id: GO:0016887
name: ATPase activity
is a: GO:0017111 ! nucleoside-triphosphatase activity
intersection of: GO:0003824 ! catalytic activity
intersection of: has input CHEBI:15377 ! water
intersection of: has input CHEBI:15422 ! ATP
intersection of: has output CHEBI:16761 ! ADP
intersection of: has output CHEBI:26020 ! phosphates
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27. Harvesting SBML
Knowledge retrieval
Query Query string # results
Contradictory defined entities Nothing 4,899
Models which represent a pro- model-of some (has-part some (has-function 54
cess involving sugar some (realized-by only (has-participant some
sugar))))
Parts of BIOMD0000000015 that part-of some BIOMD0000000015 and represents 29
represent processes involving some (has-function some (realized-by only
sugar (has-participant some sugar)))
Model entities that represent the represents some (has-part some (has-function 14
cell cycle some (realized-by only ’cell cycle’)))
Model entities that represent represents some (has-part some (’has role’ 2
mutagenic central nervous sys- some ’central nervous system drug’ and
tem drugs in the gastrointestinal ’has role’ some mutagen and part-of some
systems ’Gastrointestinal system’)
Model entities that represent represents some (has-function some 4
catalytic activity involving sugar (realized-by only (realizes some ’catalytic
in the endocrine pancreas activity’ and has-participant some (sugar
and contained-in some (part-of some
’Endocrine pancreas’)))))
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28. Conclusions
Future research
Towards integration of systems biology and biomedical ontology
extension to other modelling frameworks (CellML, FieldML, ...)
application to other resources
YeastNet
knowledge discovery
ontology of functions (of chemicals)
model comparison
model composition
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29. Conclusions
Acknowledgements
George Gkoutos
Michel Dumontier
Dan Cook
Bernard de Bono
John Gennari
Pierre Grenon
Sarala Wimalaratne
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30. Conclusions
Thank you!
Biomodels, YeastNet in OWL:
http://sbmlharvester.googlecode.com
Modularization:
http://el-vira.googlecode.com
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