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Integrating bio-ontologies with a workflow/Petri Net model to
qualitatively represent and simulate biological systems
Mor Peleg, Irene Gbashvili, and Russ Altman
Stanford University
Components of a biological modelBiological process, clinical phenotypeSequence components
Alleles, mutationsDB entries
Cellular location
Gene products
ProteolysisTransportGene regulation
Molecular function
Goals
•Piece together biological data•Develop a qualitative model at first
–Data is noisy and incomplete
•Create a quantitative model eventually
•Store knowledge to allow–systematic evaluation by scientists – input for computer algorithms
Desired properties of a biological processes model
• Represent 3 aspects of a biological system– Molecular structures, functional roles, processes
dynamics
• Include a bio-medical ontology (concept model)• Display information graphically • Support hierarchical decomposition (complexity)• Provide formal semantics to verify correctness• Simulate system dynamics• Answer biological queries (reasoning)
– Proteins with same substrates, scoped to cellular location– Alleles with roles in dysfunctional processes & disorders
Petri Nets
++I++
XML
Semi-formal
+/-I++++
statechart+/-C++++
statechart
+C++ +
Petri Nets++I++++
C+
Do other models posses the desired properties?
frames++++
frames++++
DL++
-++
Computational model
Simulation tools
verify
bio infodynamic
function
static
nesting
graf
our model + + + + I + + + Petri Nets
KIFI+
Petri Net
OPM
OMT/UML
State-charts
Workflow
BPML
Rzhetsky
EcoCyc
TAMBIS
GO
Model
PIF/PSL
C= components, I = integrated
System Architecture
Biological data
Structural Data
Dynamic data Petri Nets
OPM
Biological Process Model
Workflow Model
Process Model
Organizational Model
Biomedical Ontology
TAMBIS
UMLS
Extensions
Functional data
Framework developed in
Protégé-2000
Mapping business workflow to biological systems
Business Workflow model Biological Process Model
Process model
Structural modelOrganizational model
Biomolecular complex(Replication complex)
Biopolymer(Helicase)
Role(DNA unwinding)
member
Organizational Unit(Faculty)
Human Role(Dean)
member
Process model
(mappedto TAMBIS)
Systems modeled
• Malaria
• Translation
Peleg et al., Bioinformatics 18:825-837, 2002
Peleg et al., submitted toP IEEE
Protein translation
aa1 aa2 aa3aa4
aa5 aa6
aa7
G U
E P A
E P AtRNA0 tRNA1
tRNA0 tRNA1 tRNA2
tRNA1 tRNA2
tRNA1 tRNA2
Process Model: translation elongation
Low level Process
High level
Process
Checkpoint
Participant
process flow
substrate
product
participation
affect
inhibition
Other extensions
•Alleles and mutations•Nucleic acid 2° and 3 ° structure
tRNA mutations affect translation
aa1 aa2 aa3aa4
aa5 aa6
aa7
G U
Misreading
aa9
Frame-shiftingHalting
E P A
Participant-Role Diagrams
<role>
Individualmolecule
Complex
Collection
Functional
role
Diseaserole
Participants Relations
Rolesrole
Complex-subunit
Collection-participant
Molecule-domain
specialization
Queries
van der Aalst (1998). The Journal of Circuits, Systems and Computers 8, 21-66
P -> E A -> P
1`b
tRNA0 in EP, A occupied
tRNA1 in PE A occupied
Transient binding to A
1`b
tRNA2 in AE, P occupied
1`c
1`b
1`b
1`a
tRNA1 in PA occupied
tRNA2 in AA occupied
tRNA1 in EP
occupied
tRNA2 in PE
occupied
Binding to A-site
Ready to
bind
Free tRNA
1`a
tRNA0 in Esite
1`c
1`c
1`c
1`c
1`a
1`b
1`c
1`c
1`a
[(c<>Terminator_tRNA) and (c<>Lys_Causing_Halting)]
tRNA2 inTernary
1`bP
P
P
Val_tRNALeu_tRNAPhe_tRNA
tRNA1 in Psite
1`b
1`b
P
1`c
tRNA0exits
Mapping to Petri Nets
Simulating abnormal reading
tRNA2 in AE, P occupied
tRNA1 in Psite
tRNA0 in Esite
tRNA2 inTernary
Reading
tRNA0 in EP, A occupied
tRNA1 in PE A occupied
Misreading Frame shifting Halting
[c1] [c2] [c3] [c4]
[c2] = [(c = Misreading_tRNA)]
We also have places for nucleotides of current codons that feed in to the reading transitions[c2] = [(c = Misreading_tRNA) and (x= C) and (y = C) and (z = C)]
Normal current aa
Mutated current aa
a b c
Usefulness of Petri Nets
•Representing states explicitly•Verifying dynamic properties (Woflan)
– liveness, boundedness•Simulating dynamic behavior
(Design/CPN)•Reasoning on dynamics
–When inhibiting an activity, will we still reach a certain state?
–Do competing models have different dynamics?»Models of translation have different dynamics
Conclusion
•Our work integrates and extends three unrelated knowledge models, enabling:– representation of 3 aspects of biological
systems–reasoning on relationships among processes,
participants, and roles (queries)–simulation of system behavior under the
presence of dysfunctional components–verification of correctness (dynamic
properties)
Limitations
•Model is qualitative •Data entry is manual (no NLP)•Learning curve for using the
framework to model a new biological domain is steep
•Definition of new queries for an existing system requires use of 1st order logics
