1. 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

2. Components of a biological model Biological process, clinical phenotype Sequence components Alleles, mutations DB entries Cellular location Gene products Proteolysis Transport Gene regulation Molecular function

3. 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

4. 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

5. Petri Nets++I++ XML Semi- formal +/-I++++ statechart+/-C++++ statechart+C++ + Petri Nets++I++++ C+ Do other models posses the desired properties? frames++++ ++++ DL++ -++ Computatio nal model Simulati on tools verifybio infodynamicfunctionstaticnestinggraf 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

6. 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

7. 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 (mapped to TAMBIS)

8. Systems modeled Malaria Translation Peleg et al., Bioinformatics 18:825-837, 2002 Peleg et al., submitted to P IEEE

9. Protein translation aa1 aa2 aa3 aa4 aa5 aa6 aa7 G U EPA

10. E P A tRNA0 tRNA1 tRNA0 tRNA1 tRNA2 tRNA1 tRNA2 Process Model: translation elongation Low level Process High level Process Check point Participant process flow substrate product participation affect inhibition

11. Other extensions Alleles and mutations Nucleic acid 2° and 3 ° structure

12. tRNA mutations affect translation aa1 aa2 aa3 aa4 aa5 aa6 aa7 G U Misreading aa9 Frame-shiftingHalting EPA

13. Participant-Role Diagrams Individual molecule Complex Collection Functional role Disease role Participants Relations Roles role Complex-subunit Collection-participant Molecule-domain specialization

14. Queries

15. van der Aalst (1998). The Journal of Circuits, Systems and Computers 8, 21-66 P -> E A -> P 1`b tRNA0 in E P, A occupied tRNA1 in P E A occupied Transient binding to A 1`b tRNA2 in A E, P occupied 1`c 1`b 1`a tRNA1 in P A occupied tRNA2 in A A occupied tRNA1 in E P occupied tRNA2 in P E occupied Binding to A-site Ready to bind Free tRNA 1`a tRNA0 in E site 1`c 1`a 1`b 1`c 1`a [(c<>Terminator_tRNA) and (c<>Lys_Causing_Halting)] tRNA2 in Ternary 1`b P P P Val_tRNA Leu_tRNA Phe_tRNA tRNA1 in P site 1`b P 1`c tRNA0 exits Mapping to Petri Nets

16. Simulating abnormal reading tRNA2 in A E, P occupied tRNA1 in P site tRNA0 in E site tRNA2 in Ternary Reading tRNA0 in E P, A occupied tRNA1 in P E 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

17. 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

18. 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)

19. 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 1 st order logics

20. Thanks! peleg@smi.stanford.edu http://smi.stanford.edu/people/peleg