Integrating bio-ontologies with a workflow/Petri Net model to qualitatively represent and simulate biological systems

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

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. Do other models posses the desired properties? Model graf nesting static function dynamic bio info verify Simulation tools Computational model GO + + - TAMBIS + + DL EcoCyc + + + + frames Rzhetsky + + + + frames PIF/PSL + I KIF BPML + C XML Workflow + + + + I + + Petri Nets + State-charts + + C + statechart OMT/UML + + + + C +/- statechart OPM + + + + I +/- Semi-formal Petri Net + + I + + Petri Nets our model + + + + I + + + Petri Nets C= components, I = integrated

6. Biological Process Model Workflow Model Process Model Organizational Model Biological data Dynamic data Petri Nets Functional data OPM Biomedical Ontology TAMBIS UMLS Extensions Structural Data Framework developed in Protégé-2000 System Architecture

7. Organizational model Structural model Biomolecular complex (Replication complex) Organizational Unit (Faculty) member member Role (DNA unwinding) Biopolymer (Helicase) Role (Dean) Human Mapping business workflow to biological systems Business Workflow model Biological Process Model Process model 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 aa7 aa3 aa4 aa5 aa6 E P A G U

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

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

12. aa9 Frame-shifting Misreading Halting tRNA mutations affect translation aa1 aa2 aa7 aa3 aa4 aa5 aa6 E P A G U

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

14. Queries

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

16. tRNA1 in P site tRNA2 in Ternary tRNA0 in E site c a b [c4] [c1] [c3] [c2] Reading Frame shifting Halting Misreading Normal current aa Mutated current aa tRNA0 in E P, A occupied tRNA1 in P E A occupied tRNA2 in A E, P occupied [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)] Simulating abnormal reading

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 1st order logics

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