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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 1Physiology & Biophysics, 2Biological Structure 3Biomedical and Health Informatics University of Washington, Seattle AMIA 2008, Washington, DC Available bioinformatics for “multiscale” structure > 100 elements >> 100,000 molecule types >400 cell-part types >600 cell types 63 organ types 12 organ systems Foundational Model of Anatomy Gene Ontology Cell Type ChEBI extended from Hunter, P. J. & Borg, T. K. (2003). Nat Rev Mol Cell Biol 24(6):667-72. 2 bodies No bioinformatics for multidomain processes > 100 elements >> 100,000 molecule types Physical domains >400 cell-part types >600 cell types Domain fluids solids chemical kinetics electrochemistry diffusion heat transfer 63 organ types 12 organ systems 2 bodies Process blood flow, respiratory gas flow… myocardial contraction, leg motion… metabolism, gene expression, cell signaling… transmembrane potential, action potential… intracellular calcium dynamics… body temperature regulation… Bioinformatic problem: query process knowledge > 100 elements >> 100,000 molecule types Physical domains >400 cell-part types >600 cell types Domain fluids solids chemical kinetics electrochemistry diffusion heat transfer 63 organ types 12 organ systems 2 bodies Process blood flow, respiratory gas flow… myocardial contraction, leg motion… metabolism, gene expression, cell signaling… transmembrane potential, action potential… intracellular calcium dynamics… body temperature regulation… • How is blood pressure controlled? • Which nerves control blood pressure? Processes encoded as biosimulations models > 100 elements >> 100,000 molecule types Physical domains >400 cell-part types >600 cell types Domain fluids solids chemical kinetics electrochemistry diffusion heat transfer physics-based biosimulation model 63 organ types 12 organ systems 2 bodies Process blood flow, respiratory gas flow… myocardial contraction, leg motion… metabolism, gene expression, cell signaling… transmembrane potential, action potential… intracellular calcium dynamics… body temperature regulation… Available models constitute “physiome” > 100 elements >> 100,000 molecule types Physical domains >400 cell-part types >600 cell types Domain fluids solids chemical kinetics electrochemistry diffusion heat transfer Hunter, P. J. & Borg, T. K. (2003). Nat Rev Mol Cell Biol 24(6):667-72. 63 organ types 12 organ systems 2 bodies Process blood flow, respiratory gas flow… myocardial contraction, leg motion… metabolism, gene expression, cell signaling… transmembrane potential, action potential… intracellular calcium dynamics… body temperature regulation… Physiome Physiome problem: reuse and merge models > 100 elements >> 100,000 molecule types Physical domains >400 cell-part types >600 cell types Domain fluids solids chemical kinetics electrochemistry diffusion heat transfer 63 organ types 12 organ systems Process blood flow, respiratory gas flow… myocardial contraction, leg motion… metabolism, gene expression, cell signaling… transmembrane potential, action potential… intracellular calcium dynamics… body temperature regulation… Physiome physics-based biosimulation model Hunter, P. J. & Borg, T. K. (2003). Nat Rev Mol Cell Biol 24(6):667-72. 2 bodies Proposal for a solution: Semantics of biosimulation models can be encoded as ontologies and mapped to reference ontologies. Reference ontologies SemSim Biosimulation model code Outline: Semantics of biosimulation models can be encoded as ontologies and mapped to reference ontologies. OPB, FMA, GO, CheBI, etc. • • • • SemSim Biosimulation model code Problems: biosimulation, bioinformatics SemSim ontology Ontology of Physics for Biology (OPB) Conclusion In practice: code is hand-crafted structural knowledge physics knowledge Biophysicists and bioengineers encode physics-based mathematical models of biological processes physics-based process biosimulation fluids solids chemical kin electrochem diffusion heat transfer Time In practice: code is formal — meaning is implicit anatomical participants known only by annotation structural physiological knowledge variable names are arbitrary real Paorta(t) real PSysVein(t) real FSysArt(t) physics real Rartcap = 0.7 knowledge mmHg; mmHg; ml/sec; // Pressure of aorta // Pressure of systemic vein // Flow in systemic artery mmHg*sec/ml; // Arterial resistance FSysArt = (Paorta - PSysVein) / Rartcap; // Ohm's Law fluids solids variable chemical kin dependencies electrochem known only by diffusion annotation heat transfer In practice: multiple, incompatible languages structural knowledge physics knowledge fluids solids chemical kin electrochem diffusion heat transfer JSim, SBML, CellML, MatLab, others… physics-based process biosimulation In practice: 100’s of models in linguistic silos structural knowledge physics-based process biosimulation physics-based process biosimulation physics knowledge fluids solids chemical kin electrochem diffusion heat transfer physics-based process biosimulation CellML SBML JSim physics-based process biosimulation MatLab physics-based process biosimulation other Opportunity: a reservoir of process knowledge CellML SBML JSim MatLab other Problem: barriers to biosimulation model reuse How to find, merge and reencode models? physics-based process biosimulation CellML JSim ? SBML ? JSim ? MatLab other Problem: no access for bioinformatic queries How to query knowledge of biological processes? SparQL CellML SBML Q&A JSim MatLab other 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? Outline: Semantics of biosimulation models can be encoded as ontologies and mapped to reference ontologies. OPB, FMA, GO, CheBI, etc. • • • • SemSim Biosimulation model code Problems: biosimulation, bioinformatics SemSim ontology Ontology of Physics for Biology (OPB) Conclusion Solution: encode SemSim ontological maps… SemSim semantic maps of biosimulation models CellML SBML SemSim SemSim SemSim SemSim SemSim JSim OWL MatLab other …and annotate to reference ontologies annotate SemSim components to reference ontologies SemSim semantic maps of biosimulation models CellML SBML OPB, FMA, GO, CheBI, etc. SemSim SemSim SemSim SemSim SemSim JSim OWL MatLab other SemSim — biosimulation ontological map structural knowledge SemSim model Physical model Computational model physics knowledge fluids solids chemical kin electrochem diffusion heat transfer 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) biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : SemSim — step 1: represent math structure structural knowledge SemSim model Physical model represent variable as individuals of class Data structure physics knowledge fluids solids chemical kin electrochem diffusion heat transfer Computational model Data structure biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : SemSim — step 1: represent math structure structural knowledge SemSim model Physical model represent variable as individuals of class Data structure physics knowledge fluids solids chemical kin electrochem diffusion heat transfer Computational model Data structure use / return represent equations as individuals of class Computation Computation biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : SemSim — step 2: represent biological meaning structural knowledge SemSim model Physical model e.g., volume, physics pressure, molar flow, knowledge chemical amount fluids solids chemical kin electrochem diffusion heat transfer Physical property Computational model Data structure use / return Computation biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : SemSim — step 2: represent biological meaning structural knowledge SemSim model Physical model e.g., heart, blood in aorta, protein kinase, folate, Ca++ Computational model Physical entity has_property e.g., volume, physics pressure, molar flow, knowledge chemical amount fluids solids chemical kin electrochem diffusion heat transfer Physical property Data structure use / return Computation biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : SemSim — step 2: represent biological meaning structural knowledge SemSim model Physical model e.g., heart, blood in aorta, protein kinase, folate, Ca++ Computational model Physical entity has_property e.g., volume, physics pressure, molar flow, knowledge chemical amount fluids solids chemical kin e.g., Ohm’s law, electrochem law of mass action, diffusion mass conservation heat transfer Physical property Data structure has_player use / return Physical dependency Computation biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : Map to reference ontologies of structure structural knowledge FMA SemSim model Physical model GO Physical entity ChEBI has_property physics knowledge fluids solids chemical kin electrochem diffusion heat transfer Computational model Physical property Data structure has_player use / return Physical dependency Computation biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : Map to reference ontology of physics — OPB structural knowledge FMA SemSim model Physical model GO Physical entity ChEBI has_property physics knowledge fluids solids chemical kin OPB electrochem diffusion heat transfer Computational model Physical property Data structure has_player use / return Physical dependency Computation biosimulation code : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : Outline: Semantics of biosimulation models can be encoded as ontologies and mapped to reference ontologies. OPB, FMA, GO, CheBI, etc. • • • • SemSim Biosimulation model code Problems: biosimulation, bioinformatics SemSim ontology Ontology of Physics for Biology (OPB) Conclusion 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) 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. 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 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. 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. Physical property organizing principle Physical domain fluids volume flow pressure volume pressure momentum solids velocity force displacement solid momentum chemical kinetics molar flow chemical potential chemical amount ---- electrophysiology ionic current voltage charge ---- diffusion particle flow chemical potential particle number ---- heat flow temperature heat amount ---- heat transfer Physical property class hierarchy Physical domain fluids volume flow pressure volume pressure momentum solids velocity force displacement solid momentum chemical kinetics molar flow chemical potential chemical amount ---- electrophysiology ionic current voltage charge ---- diffusion particle flow chemical potential particle number ---- heat flow temperature heat amount ---- heat transfer Physical property by domain OPB A Flow subclass for each physical domain 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. 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. Axiomatic physical dependency Flow Constitutive physical dependency Force e.g., “Ohm’s law” Flow Constitutive physical dependency Displacement Force Force e.g., “Hooke’s law” Flow Constitutive physical dependency Displacement Force Momentum Force Flow Physical dependency class hierarchy OPB Physical dependency by domain OPB A Resistive dependency subclass for each physical domain OPB-SemSim working example SemSim model Physical model Computational model model code Physical entity : has_property Physical property Data structure has_player use / return Physical dependency Computation : : : Paorta PSysVein FSysArt Rartcap FSysArt =…. : 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) Conclusion CellML OPB FMA GO ChEBI etc. SBML SemSim SemSim SemSim SemSim SemSim JSim MatLab other 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 Partial funding from NIH MLN, MG: T15 LM007442-06 DLC, JHG: R01HL087706-01 Biophysics / biosimulation • James B. Bassingthwaighte • Herbert Sauro • Erik Butterworth • Hong Qian • Adriana Emmi • Fred Bookstein Next steps… structural knowledge SemSim model Physical model FMA GO Physical entity ChEBI has_property physics knowledge fluids Ontology ofsolids chemical kin Physics electrochem for diffusion Biology Computational model Physical property Data structure has_player use / return Physical dependency Computation : Paorta PSysVein FSysArt Rartcap : : FSysArt =…. : heat transfer access classes biosimulation code parse code SemGen write new code SemSim use-case 1: reuse legacy models VSM JSim BARO JSim VSM SemSim CV+ BARO SemSim SemSim CV JSim 1. create SemSim models of JSim biosimulation models CV+ JSim CV SemSim 3. encode merged SemSim as JSim model 2. use Prompt plug-in to Protégé to analyze and merge SemSim 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) SemSim use-case 2: reuse merged model VSM JSim 1. reuse archived BAROmodel SemSim JSim 2. create and merge CV model in different language JSim CircAdapt MATLAB CA sys art JSim VSM SemSim CV+ CV+ BARO SemSim SemSim JSim CV SemSim CA sys art CV-CA CV-CA sys art CA sys art SemSim SemSim 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) JSim SemSim use-case 3: folate chemical kinetics 1. create SemSim models of folate metabolism from published descriptions 2. merge FOL & METH SemSim models Nijhout, et al. (2004) Reed, et al. (2004) FOL FOL METH SemSim Fol-all METH SemSim Fol-all JSim SemSim FOMC FOMC Reed, et al. (2006) FOMC SemSim JSim 3. encode SemSim models in JSim; compare model output Mike Galdzicki, J. H. Gennari, M. L. Neal, D. L. Cook (work in progress) Model are complex but can be parsed physical participants real Paorta(t) real PSysVein(t) real FSysArt(t) mmHg; mmHg; ml/sec; // Pressure of aorta // Pressure of systemic vein // Flow in systemic artery real Rartcap = 0.7 mmHg*sec/ml; // Arterial resistance FSysArt = (Paorta - PSysVein) / Rartcap; // Ohm's Law Yngve, G., J. F. Brinkley, D. L. Cook, L. G. Shapiro (2007) A Model Browser for Biosimulation AMIA Annu Symp Proc (836-40) physical variables variable dependencies Ontological maps can be queried for facts aortic blood pressure vagus nerve firing rate Aortic blood pressure depends on vagus nerve firing rate.
