Towards a Semantic Systems Biology:
Biological Knowledge Management
Using Semantic Web Technologies

Erick Antezana<erick.antezana@gmail.com>

BioHackathon -Tokyo, 8 Sept. 2010

Contents
1. Introduction
2. The Cell Cycle Ontology
3. BioGateway
4. Concluding remarks
5. Future prospects

2

Contents
1. Introduction••

Motivation
Background

2. The Cell Cycle Ontology
3. BioGateway
4. Concluding remarks
5. Future prospects3Motivation• Amount of data generated in the biological experiments
continues to grow exponentially• Shortage of proper approaches or tools for analysing thisdata has created a gap between raw data andknowledge• Lack of a structured documentation of knowledge leaves
much of the data extracted from these raw data unused• Differences in the technical languages used (synonymy
and polysemy) have complicated the analysis andinterpretation of the data4

Question
What is the potential of the
Semantic Web technologies forbiological knowledge
management in the context ofa Systems Biology approach?

5

Strategy• Steps:– Problem definition: test bed case (cell cycle)– Data scaffold elements: standards,
terminologies and ontologies– Development of tools– Data integration and exploitation– Beyond cell cycle: all processes in the Gene
Ontology
6

Background• Data vs. Knowledge– Information?

• Knowledge Management– Capturing, structuring, retaining and reusing– Data integration (e.g. identity crisis)• Warehouse• Data federation7

Knowledge Representation (KR)• A formalism should– represent real world entities (in/tangible)– enable efficient organisation and processing of
information– enable shareability• Components– language– modelling principles• Interoperability– syntax (symbols + rules)– semantics (meaning)
8Same term, different concepts• “apex”– The apical meristem or its remnant on a flower– Tip of the spire of the shell of a gastropod– A town in North Carolina– A company building airplanes
–…
apex

9

Ontology• What is it? (too many definitions)– Most cited definition: “A formal specification of aconceptualisation” (Gruber, 1995)• Computer scientist– A specific artefact designed with the purpose of
expressing the intended meaning of a (shared)
vocabulary– Bio-ontologist: “A controlled vocabulary of biologicalterms and their relations” (e.g. GO, RO, PO).

• Why do we need them?
– Share and reuse information (common terminology)– Data integration– Other applications (e.g. analysis, annotation)• Multidisciplinary teams: philosophers, computerscientists, domain experts (biologists), …
10

Semantic Web• “Next generation of the current web”
• Goal: machine understandable content• Keyword search will get obsolete– Complex query formulation• Still a vision (technology under development)• Life scientists are very interested– Health Care and Life Sciences (HCLS IG - W3C)
– Several meetings, consortia, investments, etc.
11

Project

Keywords

Technologies

Website

LinkHub

document ranking, text categorisation, query
corpusRDF

Lipidbibliospherelipids, metabolites, reasoning

OWL

uniform access, package-based distribution

RDF, SPARL

http://neurocommons.org/

systems biology, cytoscape, reasoning

RDF, SPARL

http://www.bioinformatics.org/rdfscapelung cancer, omics

RDF

http://www.s3db.org/

RDF, OWL

http://swan.mindinformatics.org

Neurocommons

RDFScape

S3DB
SWAN AlzPharmneuromedicine, alzheimer, neurodegenerative
disorders

http://hub.gersteinlab.org/

SEMMASweb services, intelligent agents

OWL

http://semmas.inf.um.es/prototypes/bioinformatics.htmlSOMWebdistributed medical communities

RDF, OWL

http://www.cs.chalmers.se/proj/medview/somweb

Thea-online

protein interactions, annotations, pathways

RDF, SPARL

http://bioinfo.unice.fr:8080/thea-online/

yeast, phenotypes, interactions

OWL

http://ontology.dumontierlab.com/yowl-hcls

yOWL

12

Project

Keywords

Technologies

WebsiteBio2RDFmashup, linked data, global warehouse,
complex queries

RDF,
SPARQLhttp://bio2rdf.org/

BioDashdisease, compounds, therapeutic model,
pathway

RDF, OWL

http://www.w3.org/2005/04/swls/BioDash/Demo/

BioGateway

semantic systems biology, hypothesis
generation

RDF,
SPARQLhttp://www.semantic-systems-biology.org/biogateway/

CardioSHARE

collaborative, distributed knowledgebase,reasoning, web services

RDF,
SPARQLhttp://cardioshare.icapture.ubc.ca/

Cell Cycle
Ontology (CCO)

cell cycle, protein-protein interactions,reasoning, ontology patterns

RDF, OWL,SPARQLhttp://www.cellcycleontology.org/

CViT

cancer, tumor, gene-protein interaction
networks

RDF

https://www.cvit.org/

FungalWeb

fungal species, enzyme substrates, enzyme
modifications, enzyme retail

OWL

GenoQuerygenomic warehouse, mixed query,tuberculosis

RDF,
SPARQLhttp://www.lri.fr/~lemoine/GenoQuery/

HCLS W3C

knowledge base, life sciences, prototype

RDF, OWL,SPARQLhttp://www.w3.org/TR/hcls-kb/

Kno.e.sis

nicotine dependence, biological pathway

RDF,
SPARQL,
OWL

http://knoesis.wright.edu/research/semsci/application_
domain/sem_life_sci/bio/research/

Linked LifeData

pathways, interactions

OWL

http://www.linkedlifedata.com

13

Contents
1. Introduction
2. The Cell Cycle Ontology
3. BioGateway
4. Concluding remarks
5. Future prospects

14

Contents
1. Introduction
2. The Cell Cycle Ontology•

A knowledge base for cell cycle elucidation
Antezana E. et al. Genome Biology, 2009•http://www.cellcycleontology.org

3. BioGateway
4. Concluding remarks
5. Future prospects
15

The Cell Cycle Ontology in a nutshell• Capture knowledge of the
Cell Cycle process• “Dynamic” aspects of terms
and their interrelations• Promote sharing, reuse andenable better computational
integration with existing
resources• Issues: synonymy,
polysemy
ORGANISMS:

Where

What

“Cyclin B (what) is locatedin Cytoplasm (where)
during Interphase (when)”

When

Users:
Molecular biologist
Bioinformatician /Computational
Systems Biologist
General audience•••

Antezana E. et al. Lect. Notes Bioinformatics, 2006

Knowledge representation in CCO• Why OBO?
– “Human readable”– Standard– Tools (e.g. OBOEdit)– http://obo.sourceforge.net• Why OWL?
OWL FullOWL DLOWL Lite– Web Ontology Language– “Computer readable”– Reasoning capabilities vs. computational cost ratio– Formal foundation (Description Logics)– Tools (e.g. Protégé)• OBO2OWL mapping– ONTO-PERL (Antezana E. et al. Bioinformatics 2008)17

CCO sources• Ontologies– Gene Ontology (GO)– Relationships Ontology (RO)– Molecular Interactions (MI)– Upper level ontology (ULO)• Data sources– SWISS-PROT– GOA files– PPI: IntAct– Orthology (Decypher)

The Open Biomedical Ontologies

CCO is the composite ontology = At + Hs + Sc + Sp + orthology ; 33610 proteins in CCO

18

CCO Pipeline• ontology integration (ONTO-PERL)• format mapping• data integration• data annotation• consistency checking• maintenance• data annotation• semantic improvement: OPPL
(Egaña, M., Stevens, R. Antezana, E. OWL-ED, 2008)• ODP (Egaña, M., Antezana, E., et al. BMC Bioinf. 2008)19

Sample knowledge in CCO

20

Exploring CCO (1/2)

OBO-Edit

Protégé

21
CytoscapevisANT

Exploring CCO (2/2)

CCO website (SPARQL)OWLDoc server

22
BioPortal

Ontology Look up Service

Advanced Querying• RDF = Resource Description Framework– Metadata model: elements = resources• It allows expressing knowledge about web resources instatements made of triples (basic information unit) :Subject–-

Predicate–-Object• Subject corresponds to the main entity that needs to be
described.
• Predicate denotes a quality or aspect of the relation
between the Subject and Object.
• Example: “The protein DEL1 is located in the nucleus”• It “means” something…
23

SPARQL*• Query RDF models (graphs)• Powerful, flexible• Its syntax is similar to the oneof SQL.
• Virtuoso Open Server• Example (matching two triples):?protein sp:is_a sp:CCO_B0000000 .
?protein rdfs:label ?protein_label* http://www.w3.org/TR/rdf-sparql-query/

24

25

“all the core cell cycle
proteins (S.pombe)
participating in a known
process”

26

prot_namebiological_process_name

UBC11_SCHPO

G2%2FM transition of mitotic cell cycle

UBC11_SCHPO

cell cycle

UBC11_SCHPO

mitosisUBC11_SCHPO

mitotic metaphase%2Fanaphase transition

UBC11_SCHPO

regulation of mitotic cell cycle

UBC11_SCHPO

cyclin catabolic process

SRW1_SCHPO

cell cycle

SRW1_SCHPO

cyclin catabolic process

SRW1_SCHPO

activation of anaphase-promoting complex during mitotic cell cycle

SRW1_SCHPO

cell cycle arrest in response to nitrogen starvation

SRW1_SCHPO

negative regulation of cyclin-dependent protein kinase activity

DYHC_SCHPO

dhc1-peg1-1 physical interaction

DYHC_SCHPO

synapsis

DYHC_SCHPO

meiotic recombination

DYHC_SCHPO

horsetail nuclear movement

ORB6_SCHPO

cell morphogenesis checkpoint

ORB6_SCHPO

regulation of cell cycle

DED1_SCHPO

G2%2FM transition of mitotic cell cycle

27

Reasoning over CCO••••

OWL-DL: balance tractability with expressivity
Consistency checking: no contradictory facts
Classification: implicit2explicit knowledge
Tools: Protégé, Reasoners (e.g. RACER, Pellet)• Sample Query– “Which cell cycle related proteins participate in a reported interaction?”
protein andparticipates_in some interaction
participates_in
participates_in

Answer:participates_in
participates_in
participates_in

participates_in

participates_inproteininteraction

28

Cellular localization checks• Query: “If a protein is cell cycle regulated, it must not be located in thechloroplast (IDEM: mitochondria)” (RACER*)

29* http://www.racer-systems.com

Conclusions• Adequate knowledge representation:– enables automated reasoning (manyinconsistencies were detected)– simple biological hypothesis generation• Data integration based on trade-offs (e.g.
multiple inheritance)• Performance issues (technology limitations)30

Contents
1. Introduction
2. The Cell Cycle Ontology
3. BioGateway•

An integrative approach for supporting
Semantic Systems Biology
Antezana et al. BMC Bioinformatics, 2009•http://www.SematicSystemsBiology.org

4. Concluding remarks
5. Future prospects
31

BioGateway• From “cell cycle” to the entire set of processes
in the Gene Ontology• CCO: deep downwards (coverage)• BioGateway: broad coverage• BioGateway’s goal: build “complex” queries
over the entire set of organisms annotated by
the GOA• Support a Semantic Systems Biology approach

Antezana et al. BMC Bioinformatics, 200932

Systems Biology• Yet another definition• Key: system• What is a system?
• System =– set of elements,– dynamically interrelated,– having an activity,– to reach an objective (sub-aims),– INPUT: data/energy/matter– OUTPUT: information/energy/matter
33

Systems Biology (cont)• “A system (and its properties) cannot be
described in terms of their terms in
isolation; its comprehension emerges
when studied globally”• Systems Biology = Approach to study
biological systems.
• Arbitrary borders• A system within a system
34Systems Biology (cont)• Types of systems biology:– “Standard/Classical” Systems Biology(Kitano, Science 2002. Sauer et al, Science 2007)– Translational Systems Biology(Vodovotz, PLoS Comp Biol 2008)– Semantic Systems Biology(Antezana et al, Briefings in Bioinformatics 2009)35

Semantic Systems Biology• Semantic?
– New emerging technologies for analyzing data
and formalizing knowledge extracted from it• A new paradigm elements:– Knowledge representation– Reasoning ==> hypothesis– Querying

36Biological
Mathematicalknowledge
model
Consistency checking
New information to model
Querying
Model Refinement
Automated reasoning

Data
analysisextraction,
Information
Information
extraction
Knowledge formalization

Semantic
Systems
Biology Cycle
Experimentation,
Data generation

Dynamical
simulations
andHypothesis
formulation
hypothesis
formulation
Experimental
designExperimental design

BioGateway: a tool to support
Semantic Systems Biology• Automatic data integration pipeline (~8 months)• Quick query results: performance, choice:“tuned” RDF (no OWL), 1 graph per resource• Human “readable” output:– labels, no IDs or URI…

• Good practice:– Standards (RDF) => orthogonality, …
– Representation issues (e.g. n-ary relations)• Transitive closure:– is_a (subsumption relation), part_of (partonomy)

38Transitive closure graphs• If A part_of B, and B part_of C, then A part_of C isalso added to the graph.
• Many interesting queries can be done in a performant
way with it, like 'What are the proteins that arelocated in the cell nucleus or any subpartthereof?'• The graphs without transitive closure are available
for querying as well.

Blondé, W., Antezana E. et al. ICBO, 200939

BioGateway pipeline
•
•
•
•
•
•
•
•

1 Swiss-Prot file, the section of UniProt
KB of proteins
1 NCBI file with the taxonomy oforganisms
1 Metaonto file with information about
OBO Foundry ontologies
2 Metarel files with relation typeproperties
5 CCO files with integrated information
about cell cycle proteins
44 OBO Foundry files with diverse
biomedical information + Transitive
Closure51 Transitive Closure files to enhancequery abilities
893 GOA files with GO annotations

40

BioGateway holds ~175 million RDF triples!!!

Sample RDF-ication: GOA
UniProtKB O03042 O03042 GO:0000287 GOA:spkw|GO_REF:0000004 IEA• Protein: Ribulose bisphosphate carboxylase large chain (O03042)• GO term (MF): Magnesium Ion Binding (GO:0000287)• Therefore, O03042 has the molecular function of binding magnesium ion.
• This fact is supported by IEA, that is, Inferred from Electronic Annotation.”41

A library of queries*• The drop-down box contains 35 queries:– 14 protein-centric biological queries:• The role of proteins in diseases• Their interactions• Their functions• Their locations• …

– 21 ontological queries:• Browsing abilities in RDF like getting the neighborhood, thepath to the root, the children,...
• Meta-information about the ontologies, graphs, relations• Queries to show the possibilities of SPARQL on BioGateway,
like counting, filtering, combining graphs,...
• …
42* http://www.semantic-systems-biology.org/biogateway/querying

Select a query in thedrop-down box

The query editor

43

Parameterising the query

44

The resultsappear in a
separate
window45

http://www.semantic-systems-biology.org/sparql-viewer/

The URIs in blue.

The result:9 proteins
Labeled arrows
to extra 46
information

Conclusions / Results• BioGateway: RDF store for Biosciences (prototype!)• Data integration pipeline: BioGateway• Queries and knowledge sources and system design
go hand-in-hand (user interaction)• Enables building relatively “complex” questions• Existing integration obstacles due to:• diversity of data formats• lack of formalization approaches• Semantic Web technologies add a new dimension ofknowledge integration to Systems Biology
47

Contents
1. Introduction
2. The Cell Cycle Ontology
3. BioGateway
4. Concluding remarks
5. Future prospects

48

Conclusions and prospects• Categories:– Way of computationally representing biological knowledge– Exploitation of such knowledge• Both gave rise to a new (complementary) form of
Systems Biology: Semantic Systems Biology
approach– Data integration– Holistic (systemic) approach– Data exploitation (e.g. querying, reasoning)– Ultimately, create new hypothesis• Semantic Web technologies do have the potential toprovide a sound framework for biological data
integration
49

Contents
1. Introduction
2. The Cell Cycle Ontology
3. BioGateway
4. Concluding remarks
5. Future prospects

50

Future prospects• Temporal representation• Capture non-crisp knowledge (e.g.
protein sar1 is usually located in the
nuclear membrane)• Integration of multimedia (images, videos)• “Deep down” integration of data into
BioGateway (like in CCO)
• …
51

Acknowledgements
•
•
•
•
•
•
•
•
•
•
•
•

Martin Kuiper (NTNU, NO)
Vladimir Mironov (NTNU, NO)
Mikel Egaña (U Manchester, UK / U Murcia, ES)
Robert Stevens (U Manchester, UK)
BioHealth Group (U Manchester, UK)
Ward Blondé (U Ghent, BE)
Bernard De Baets (U Ghent, BE)
Alistair Rutherford (UK)
Alan Ruttenberg (Science Commons, US)
Ontology and Semantic Web community
Users, (former/new) colleagues and friends…

http://www.semantic-systems-biology.org
52

Extra slides
53

BioGateway

The homepage of SSB, including BioGateway as afirst step towards this approach

54

Use the buttons forprefixes and otherconstructs

Type a query here.

Click Run!
55

Prospective users• Molecular biologist: interacting

components, events, roles thateach component play. Hypothesis
evaluation.

• Bioinformatician/Computational

Systems Biologist: data
integration, annotation, modelingand simulation.

• General audience: educational
purposes.

56

Resources• Open Biomedical Ontologies (OBO)– About 60 bio-ontologies (mainly OBOF)– OBO Foundry– Multidisciplinary teams: philosophers, computerscientists, domain experts (biologists), …

• Tools (OBO-Edit, Protégé, etc.)• Data centres (academy/industry) “migrating”
towards ontology-aware resources57

Format mapping: OBOOWL• Mapping not totally biunivocal; however, all the data has
been preserved.
• Missing properties in OWL relations:• reflexivity,• asymmetry,• Intransitivity, and• partonomic relationships.

• Existential and universal restrictions cannot be explicitly
represented in OBO => Consider all as existential.
• CCO in OWL is in sync with the NCBO mapping (DL)• Mapping efforts:• http://spreadsheets.google.com/ccc?key=pWN_4sBrd9l1Umn1LN8WuQQ• http://www.psb.ugent.be/cbd/cco/OBO2OWL%20Mappings.pdf
58

OWL restrictions

Restriction on Nucleus: some part_of Cell

Necessary conditions vs Necessary and sufficient conditions

59

Sample entry in OBO

60

Sample entry in OWL61

OBO2OWL mapping sample

62

CCO checked with…
validator

Source tab

editoroutlineSWeDE Eclipse plug-in: http://owl-eclipse.projects.semwebcentral.org 63

Checked with…

Protégé: http://protege.stanford.edu/
A reasoner (RACER) was used to identify those inconsistencies
64and with…

Vowlidator: http://projects.semwebcentral.org/projects/vowlidator/)

65

OPPL in CCO

66

Egaña, M., Stevens, R. Antezana, OWL-ED, 2008

Sample model

67

Other sample query in OWL• Entities that are the location of proteinsparticipating in the S-phase (CCO_P0000014)
or any process which is part of it.?
location_of some (participates_in some (CCO_P0000014 or (part_of some CCO_P0000014)))68

Initial question (revisited)“to what extent can current Semantic Web
technologies support biological knowledge
management for basic or complex querying, forautomated reasoning and inferencing, specificallyin the context of a Systems Biology approach
where integrated knowledge could be utilised toaddress the relations between components of thecell cycle control mechanism, their involvement in(sub)modules of cell cycle control, and theirpotential place in overall network topology?”69

Mathematical model
New information to model
Model Refinement

Data analysis
Information extraction

Systems
Biology Cycle
Experimentation,
Data generation

Dynamical simulations andhypothesis formulation
Experimental designBiological knowledge
Consistency checking
Querying
Automated reasoning

Information extraction,
Knowledge formalization

Semantic
Systems
Biology Cycle
Experimentation,
Data generation

Hypothesis formulation
Experimental design

BioGateway• Automatic pipeline– Run on a regular basis (~6 months)– Latest data available (from scratch)• Uses Virtuoso Open Server– Open Source software that can host a triple store– Can build this from RDF files– Has a DB backend• Supports SPARQL*

http://www.openlinksw.com/virtuoso/*http://www.w3.org/TR/rdf-sparql-query/72

BioGateway graphs

Each RDF-resource in BioGateway has a URI of this form:
http://www.semantic-systems-biology.org/SSB#resource_id
Each RDF-graph in BioGateway has a URI of this form:
http://www.semantic-systems-biology.org/graph_name73All the queries are explained in a tutorial*

For every query the name, theparameters and the function are
indicated at the top.

The parameters areindicated in red.

74
* http://www.semantic-systems-biology.org/biogateway/tutorial998 RDF-files can be
downloaded from theResources pageThe graphnames canbe used toquery orcombineindividual
graphs forquickeranswers or
more
specific
information

75

The neighbourhood of the human protein 1443F in the RDF-graph

The resulting triples (arrows)
are represented as a small
grammatical sentence:subject, predicate, object.

Outgoing arrows

Incoming arrows
76Principles
1. Orthogonality
2. A “common language” (e.g. RDF)
3. Unique ID + resolution (e.g. purl.org)
4. Comply to: ULO (e.g. BFO), RO, …
5. Explicit semantics
6. Rich axiomatisation
7. Application-driven development (e.g. SB)
8. Peer review (community evaluation)
9. Tooling (e.g. visualisation)
10. Licensing (e.g. CC)
77

Metarel• Metarel is a generic ontological hierarchy for
relation types, consistent with OBOF and RDF.
• It includes meta-information like transitivity,
reflexivity and composition.
• BioMetarel includes all the biological relation
types that are used in BioGateway.
• We are still testing the exploitation ofcomposition, like A located in B and B part ofC, gives A located in C.
Blondé, W., Antezana et al. BMC Bioinformatics, 200978

The RDF export specifications• The RDF is automatically generated with ontoperl, our own ontology API.
• Many choices for the RDF specifications weremade during the testing of the queries.
• The resources are available either as part of an
integrated graph or as individual graphs.
• BioMetarel, a relation ontology, provides labels
for the URIs of the relations.
• OWL(XML/RDF) was avoided because it is too
verbose. We preferred RDF optimized forquerying.
79

Next steps• More data sources (e.g. Nutrigenomics, pathways
etc.)• RDF rules (e.g. RuleML)• A more user-friendly interface• Reasoning• OBO cross products• …

80Systems biology paradigm
top-down and bottom-up modeling
top-down
data driven

bottom-up
hypothesis driven

Biological Process
Genome-scale
functional
genomics data

Predictive
mathematicalmodel

Statistics
MiningKnowledge
Mathematics

Gene
networkcomponents
Knowledge
Management

81Discussion• W3C standards – limitations (e.g. spatiotemporal information, microarrays experiments)• Biological identifiers: URIs, LSIDs, MIRIAM
URIs, etc. They should be scalable & resolvable.
• Lack of semantic content: poor axiomatisation,
inadequately codified. Use of standard
languages (e.g. RDF).
• Adequate tools: not adapted for real-size
problems (e.g. reasoning). Designed with auniversal architecture in mind.
82

SSB at a community level*• Semantic bio-content: encourage andfacilitate• Best practices for such creation (standards)• Mechanism for identifying biological entities• Bridge semantic technology developers andlife scientists (=the users)
WIKI: http://www.bio.ntnu.no/systemsbiology/ssbwiki/doku.php

83

Conclusions / Results• Data integration pipeline: life cycle of the KB• Existing integration obstacles due to:• diversity of data formats• lack of formalization approaches• Reasoning services: inconsistency checks,
classification => hypothesis• Trade-offs: complex queries, representational
issues84Current issues• Temporal & spatial representation– OBOF not enough…

• Performance (reasoners)– Huge ontologies• Weighted knowledge (often, sometimes)

85

CCO accession number

CCO:[CPFRTIBGOU]nnnnnnn
namespacesub-namespace

7 digits

C: cellular component
P: biological process
F: molecular function
R: reference
T: taxon
I: interaction
B: protein
G: gene
O: ortholog
U: upper-level term

Example: CCO: P0000056“cell cycle”
86Upper Level Ontology for
Application Ontologies

87DIAMONDS platform *88* EU projectExample: checking the single
inheritance principle• Principle: “No class in a classification should have•••more than one is_a parent on the immediate higher
level” (Smith B. et al.)Detect the relationships which violate that rule using
a reasoner (RACER*)
Solution: disjoint among the terms at the same levelof the structure
32 problems found:
4: “part_of” instead of “is_a”
18: should stay without any change (FP)
10: not consistent (used terminology)•••* http://www.racer-systems.com

89

part_of instead of is_a

The sub-ontology on the left has inconsistent relation s4(is_a) which has been changed into part_of (right side) .

90