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Bridging Biological Ontologies and Biosimulation: The Ontology of Physics for Biology

Bridging Biological Ontologies and Biosimulation: The Ontology of Physics for Biology. Daniel L. Cook 1, 2 John H. Gennari 3 Jose L. V. Mejino 2 Maxwell L. Neal 3. 1 Physiology & Biophysics, 2 Biological Structure 3 Biomedical and Health Informatics University of Washington, Seattle.

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Bridging Biological Ontologies and Biosimulation: The Ontology of Physics for Biology

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  1. Bridging Biological Ontologies and Biosimulation:The Ontology of Physics for Biology Daniel L. Cook1, 2 John H. Gennari 3 Jose L. V. Mejino 2 Maxwell L. Neal 3 1Physiology & Biophysics, 2Biological Structure 3Biomedical and Health Informatics University of Washington, Seattle AMIA 2008, Washington, DC

  2. Available bioinformatics for “multiscale” structure 12 organ systems > 100 elements >> 100,000 molecule types >400 cell-part types >600 cell types 63 organ types 2 bodies Foundational Model of Anatomy Cell Type Gene Ontology ChEBI extended from Hunter, P. J. & Borg, T. K. (2003). Nat Rev Mol Cell Biol 24(6):667-72.

  3. Domain Process fluids blood flow, respiratory gas flow… Physical domains solids myocardial contraction, leg motion… chemical kinetics metabolism, gene expression, cell signaling… electrochemistry transmembrane potential, action potential… diffusion intracellular calcium dynamics… body temperature regulation… heat transfer No bioinformatics for multidomain processes 12 organ systems > 100 elements >> 100,000 molecule types >400 cell-part types >600 cell types 63 organ types 2 bodies

  4. Domain Process fluids blood flow, respiratory gas flow… Physical domains solids myocardial contraction, leg motion… chemical kinetics metabolism, gene expression, cell signaling… electrochemistry transmembrane potential, action potential… diffusion intracellular calcium dynamics… body temperature regulation… heat transfer Bioinformatic problem: query process knowledge 12 organ systems > 100 elements >> 100,000 molecule types >400 cell-part types >600 cell types 63 organ types 2 bodies • How is blood pressure controlled? • Which nerves control blood pressure?

  5. Processes encoded as biosimulations models 12 organ systems > 100 elements >> 100,000 molecule types >400 cell-part types >600 cell types 63 organ types 2 bodies Domain Process fluids blood flow, respiratory gas flow… Physical domains solids myocardial contraction, leg motion… chemical kinetics metabolism, gene expression, cell signaling… electrochemistry transmembrane potential, action potential… diffusion intracellular calcium dynamics… body temperature regulation… heat transfer physics-based biosimulation model

  6. Available models constitute “physiome” 12 organ systems > 100 elements >> 100,000 molecule types >400 cell-part types >600 cell types 63 organ types 2 bodies Domain Process fluids Physiome blood flow, respiratory gas flow… Physical domains solids myocardial contraction, leg motion… chemical kinetics metabolism, gene expression, cell signaling… electrochemistry transmembrane potential, action potential… diffusion intracellular calcium dynamics… body temperature regulation… heat transfer Hunter, P. J. & Borg, T. K. (2003). Nat Rev Mol Cell Biol 24(6):667-72.

  7. Physiome problem: reuse and merge models 12 organ systems > 100 elements >> 100,000 molecule types >400 cell-part types >600 cell types 63 organ types 2 bodies Domain Process fluids Physiome blood flow, respiratory gas flow… Physical domains solids myocardial contraction, leg motion… chemical kinetics metabolism, gene expression, cell signaling… electrochemistry transmembrane potential, action potential… diffusion intracellular calcium dynamics… body temperature regulation… heat transfer physics-based biosimulation model Hunter, P. J. & Borg, T. K. (2003). Nat Rev Mol Cell Biol 24(6):667-72.

  8. Reference ontologies SemSim Biosimulation model code Proposal for a solution: Semantics of biosimulation models can be encoded as ontologies and mapped to reference ontologies.

  9. OPB, FMA, GO, CheBI, etc. SemSim Biosimulation model code Outline: Semantics of biosimulation models can be encoded as ontologies and mapped to reference ontologies. • Problems: biosimulation, bioinformatics • SemSim ontology • Ontology of Physics for Biology (OPB) • Conclusion

  10. fluids solids chemical kin electrochem diffusion Time® heat transfer In practice: code is hand-crafted Biophysicists and bioengineers encode physics-based mathematical models of biological processes structural knowledge physics-based process biosimulation physics knowledge

  11. fluids solids chemical kin electrochem diffusion heat transfer In practice: code is formal — meaning is implicit anatomical participants known only by annotation structural knowledge physiological variable names are arbitrary real Paorta(t)   mmHg; // Pressure of aorta real PSysVein(t)   mmHg;   // Pressure of systemic vein real FSysArt(t) ml/sec; // Flow in systemic artery real Rartcap = 0.7 mmHg*sec/ml;  // Arterial resistance FSysArt = (Paorta - PSysVein) / Rartcap; // Ohm's Law physics knowledge variable dependencies known only by annotation

  12. fluids solids chemical kin electrochem diffusion heat transfer In practice: multiple, incompatible languages JSim, SBML, CellML, MatLab, others… structural knowledge physics-based process biosimulation physics knowledge

  13. physics knowledge fluids solids chemical kin electrochem diffusion heat transfer In practice: 100’s of models in linguistic silos CellML physics-based process biosimulation structural knowledge SBML physics-based process biosimulation JSim physics-based process biosimulation MatLab physics-based process biosimulation other physics-based process biosimulation

  14. Opportunity: a reservoir of process knowledge CellML SBML JSim MatLab other

  15. Problem: barriers to biosimulation model reuse CellML JSim How to find, merge and re-encode models? SBML ? physics-based process biosimulation JSim ? MatLab ? other

  16. Problem: no access for bioinformatic queries CellML SparQL How to query knowledge of biological processes? SBML JSim Q & A MatLab other

  17. Two fields, two problems: Biosimulation — re-use biosimulation models • Find models of blood pressure control. • Which models include neural-control? Bioinformatics — query process knowledge • How is blood pressure controlled? • Which nerves control blood pressure?

  18. OPB, FMA, GO, CheBI, etc. SemSim Biosimulation model code Outline: Semantics of biosimulation models can be encoded as ontologies and mapped to reference ontologies. • Problems: biosimulation, bioinformatics • SemSim ontology • Ontology of Physics for Biology (OPB) • Conclusion

  19. SemSim SemSim SemSim SemSim SemSim OWL Solution: encode SemSim ontological maps… CellML SemSim semantic maps of biosimulation models SBML JSim MatLab other

  20. SemSim SemSim SemSim SemSim SemSim OWL …and annotate to reference ontologies CellML SemSim semantic maps of biosimulation models annotate SemSim components to reference ontologies SBML OPB, FMA, GO, CheBI, etc. JSim MatLab other

  21. physics knowledge fluids solids chemical kin electrochem diffusion heat transfer SemSim — biosimulation ontological map structural knowledge SemSim model Physical model Computational model biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : Gennari, J. H., M. L. Neal, B. E, Carlson, D. L. Cook (2008) Integration of multi-scale biosimulation models via light-weight semantics Pac Symp Biocomput (414-425)

  22. Data structure physics knowledge fluids solids chemical kin electrochem diffusion heat transfer SemSim — step 1: represent math structure structural knowledge SemSim model Physical model Computational model biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : represent variable as individuals of class Data structure

  23. Data structure physics knowledge fluids Computation solids chemical kin electrochem diffusion heat transfer SemSim — step 1: represent math structure structural knowledge SemSim model Physical model Computational model biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : represent variable as individuals of class Data structure use / return represent equations as individuals of class Computation

  24. structural knowledge Data structure physics knowledge fluids Computation solids chemical kin electrochem diffusion heat transfer SemSim — step 2: represent biological meaning SemSim model Physical model Computational model biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : Physical property e.g., volume, pressure, molar flow, chemical amount use / return

  25. structural knowledge Data structure physics knowledge fluids Computation solids chemical kin electrochem diffusion heat transfer SemSim — step 2: represent biological meaning SemSim model Physical model Computational model biosimulation code e.g., heart, blood in aorta, protein kinase, folate, Ca++ Physical entity : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : has_property Physical property e.g., volume, pressure, molar flow, chemical amount use / return

  26. structural knowledge Data structure physics knowledge fluids Computation solids Physical dependency chemical kin electrochem diffusion heat transfer SemSim — step 2: represent biological meaning SemSim model Physical model Computational model biosimulation code e.g., heart, blood in aorta, protein kinase, folate, Ca++ Physical entity : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : has_property Physical property e.g., volume, pressure, molar flow, chemical amount has_player use / return e.g., Ohm’s law, law of mass action, mass conservation

  27. Data structure physics knowledge fluids Computation solids Physical dependency chemical kin electrochem diffusion heat transfer Map to reference ontologies of structure structural knowledge SemSim model Physical model Computational model FMA biosimulation code Physical entity GO : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : ChEBI has_property Physical property has_player use / return

  28. Data structure physics knowledge fluids Computation solids Physical dependency chemical kin electrochem diffusion heat transfer Map to reference ontology of physics — OPB structural knowledge SemSim model Physical model Computational model FMA biosimulation code Physical entity GO : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : ChEBI has_property Physical property has_player use / return OPB

  29. OPB, FMA, GO, CheBI, etc. SemSim Biosimulation model code Outline: Semantics of biosimulation models can be encoded as ontologies and mapped to reference ontologies. • Problems: biosimulation, bioinformatics • SemSim ontology • Ontology of Physics for Biology (OPB) • Conclusion

  30. OPB foundational theory — system dynamics • Engineering system dynamics • Bond graph theory • Karnopp, Margolis, Rosenberg (1968) • EngMath - Ontology for Engineering Mathematics • Gruber, Olsen (1994) • PHYSYS - Physical Systems Ontology Borst, Top, Akkermans (1994) • Biochemical system dynamics • Network thermodynamics • Oster, Perelson, Katchalsky (1971) • Mickulecky (1983) • Beard, Qian (2008)

  31. OPB representational goals • Represent abstractions used in physics-based biosimulations—not a theory of “reality”. • Adhere to OBO principles. • Implement in OWL; deploy to OBO and BioPortal.

  32. OPB:Physics analytical entity A Physics analytical entity is an abstraction of the real world created within the science of classical physics for the description of physical entities and the analysis of physical processes. OPB

  33. OPB:Physical entity A Physics analytical entity is an abstraction of the real world created within the science of classical physics for the description of physical entities and the analysis of physical processes. OPB A Physical entity is a spatial, temporal, or energetic abstraction of the physical world.

  34. OPB:Physical property A Physics analytical entity is an abstraction of the real world created within the science of classical physics for the description of physical entities and the analysis of physical processes. OPB A Physical entity is a spatial, temporal, or energetic abstraction of the physical world. A Physical property is a quantifiable attribute of a physical entity whose value can be determined by physical measurement at a moment in time.

  35. Physical property organizing principle Physical domain fluids solids chemical kinetics electrophysiology diffusion heat transfer

  36. Physical property class hierarchy Physical domain fluids solids chemical kinetics electrophysiology diffusion heat transfer

  37. Physical property by domain OPB A Flow subclass for each physical domain

  38. Physical dependency A Physics analytical entity is an abstraction of the real world created within the science of classical physics for the description of physical entities and the analysis of physical processes. OPB A Physical entity is a spatial, temporal, or energetic abstraction of the physical world. A Physical property is a quantifiable attribute of a physical entity whose value can be determined by physical measurement at a moment in time. A Physical dependency is a quantitative dependency between the magnitudes of two or more physical properties according to a physical law.

  39. Physical dependency organizing principle A Physical dependency is a quantitative dependency between the magnitudes of two or more physical properties according to a physical law.

  40. Axiomatic physical dependency

  41. Flow Constitutive physical dependency Force e.g., “Ohm’s law”

  42. Flow Displacement Force Constitutive physical dependency Force e.g., “Hooke’s law”

  43. Flow Displacement Force Momentum Flow Constitutive physical dependency Force

  44. Physical dependency class hierarchy OPB

  45. Physical dependency by domain OPB A Resistive dependency subclass for each physical domain

  46. Data structure Computation Physical dependency OPB-SemSim working example SemSim model Physical model Computational model model code Physical entity : Paorta PSysVein FSysArt Rartcap : : : FSysArt =…. : has_property Physical property has_player use / return Neal, M. L., J. H. Gennari, T. Arts, D. L. Cook (2009) Advances in semantic representation of multiscale biosimulations: A case study in merging models Pac Symp Biocomput (in press)

  47. SemSim SemSim SemSim SemSim SemSim Conclusion CellML SBML OPB FMA GO ChEBI etc. JSim MatLab other

  48. Acknowledgements SemSim / OPB team • Maxwell L. Neal (Grad student) • Michal Galdzicki (Grad student) • John H. Gennari, PhD (Assoc Prof) • Daniel L. Cook, MD, PhD (Res Prof) UW contributors Bioinformatics • Cornelius Rosse • Onard Mejino • James Brinkley • Todd Detwiler Biophysics / biosimulation • James B. Bassingthwaighte • Herbert Sauro • Erik Butterworth • Hong Qian • Adriana Emmi • Fred Bookstein Partial funding from NIH MLN, MG: T15 LM007442-06 DLC, JHG: R01HL087706-01

  49. Data structure physics knowledge fluids Computation solids Physical dependency chemical kin electrochem diffusion heat transfer Next steps… structural knowledge SemSim model Physical model Computational model FMA biosimulation code Physical entity GO : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : ChEBI has_property Physical property use / return has_player Ontology of Physics for Biology SemGen parse code access classes write new code

  50. CV+ JSim VSM BARO CV JSim JSim JSim SemSim use-case 1: reuse legacy models VSM SemSim CV+ BARO SemSim SemSim CV 3. encode merged SemSim as JSim model SemSim 2. use Prompt plug-in to Protégé to analyze and merge SemSim models 1. create SemSim models of JSim biosimulation models Gennari, J. H., M. L. Neal, B. E, Carlson, D. L. Cook (2008) Integration of multi-scale biosimulation models via light-weight semantics Pac Symp Biocomput (414-425)

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