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

Thanks!

peleg@smi.stanford.edu

http://smi.stanford.edu/people/peleg