1st Edition
The Ontology of Physics for Biology Semantic Modeling of Multiscale, Multidomain Physiological Systems
Rationale
Preface – What We Are Talking About, and Why
Glossary
Chapter 1 ◾ Biomedical Challenges
REAL-WORLD USE-CASES – THE NEED FOR UNDERSTANDING
SCOPE OF THE MULTIDOMAIN, MULTISCALE CHALLENGE
Types of Things (Continuants)
Types of Processes (Occurrents)
Physiological Domains
Physical Property Measures and Analysis
Understanding Systems, Predicting Outcomes
The Challenges
USE CASE 1: HYPERTENSION AS FAILED BLOOD PRESSURE CONTROL
Arterial Hypertension – Anatomy and Physiology
The Cardiac Cycle
Blood Flow through the Arteries, Arterioles, and Capillaries
Pulsatile Blood Pressure and Flow through the Vascular System
Systemic Control of Blood Pressure and Flow
USE CASE 2: DIABETES MELLITUS AS FAILED BLOOD SUGAR CONTROL
Clinical Test for Elevated Blood Sugar
Multiscale, Multidomain Pathophysiology of Diabetes
Pancreatic Hormones as Blood Glucose Controllers
Cellular Metabolic Energy Production and Regulation
Systemic Control of Metabolism
Disease as Failure of Feedback Control
CHALLENGES OF MULTISCALE, MULTIDOMAIN ANALYSIS
Domain Technology “Silos”
The Systems Perspective
Challenges of Feedback and Homeostasis
Disease as Feedback Control Failures
Need for Informatics and Analysis
HYPOTHESIS EXPRESSION AND ANALYSIS
Narratives, Diagrams, and Computations
Qualitative Functional Reasoning
Quantitative Analysis and Simulation
CHALLENGES – NEED FOR INFORMATION, KNOWLEDGE, AND ANALYSIS
Chapter 2 ◾ Biomedical Information and Data Resources
BIOINFORMATICS RESOURCES: PHYSICAL ENTITIES
Anatomy Resources
Tissue and Cell Type Resources
Cellular Components
Proteins
Small Chemical Species
BIOINFORMATICS RESOURCES: PHYSICAL PROCESSES
Enzymatic Biochemical Reactions and EC Numbers
Protein–Protein Interaction Databases
Gene Ontology: Biological Process and Molecular Function
Pathway Resources
Quantitative Biosimulation Model Resources
PHYSICAL PROPERTIES FOR PHYSIOLOGY
VISUALIZING BIOLOGICAL PROCESSES
PUTTING IT TOGETHER: THE PHYSIOME VISION
Chapter 3 ◾ Biomedical Ontologies
ONTOLOGY – THINGS, RELATIONS, CLASSES, INSTANCES
AN EXAMPLE ONTOLOGY: THE FOUNDATIONAL MODEL OF ANATOMY
ONTOLOGIES: WHY?
ONTOLOGY QUALITY
UPPER-LEVEL ONTOLOGIES
Basic Formal Ontology
Relations Ontology (RO)
OPB – THE QUANTITIES AND DEPENDENCIES OF CLASSICAL PHYSICS
Chapter 4 ◾ Biophysical Systems Analysis
“MODEL”?
Memory – Recall as Prediction
Mimicry – Modeling “As If”
Mechanism – Testing Physics-Based Hypotheses
PHYSIOLOGICAL SYSTEMS ACROSS MULTIPLE SCALES
Chemical Reaction Processes
Molecular Signaling Pathways
Organ System Processes
REPRESENTING BIOPHYSICAL AND PHYSIOLOGICAL KNOWLEDGE
CRAFT OF QUANTITATIVE MODELING OF MECHANISM
Mathematics – Differential and Integral Calculus
Variables vs. Parameters
Getting the Best-Fit – Parameter Optimization and Sensitivity
MODELING SCOPE AND SCALE
Space and Time Are Continuous and Unbounded
Object vs. Process Models
Collections of Discrete Things
KINDS OF PHYSICAL MEASURES
Physical Measures in Biophysics and Engineering
Biological Measures Present Quantification Issues
Continuous vs. Discrete Measures
Categorical Measures
Population Measures
ATTRIBUTES OF PHYSICAL MEASURES
Precision vs. Accuracy
Number Forms
Units of Measure
Property Dimensions
Notations of Scale
Normalized and Dimensionless Quantities
Extensive vs. Intensive Measures
SUMMARY, NEXT STEPS
Chapter 5 ◾ System Dynamic Modeling
PRINCIPLES OF SYSTEM DYNAMICS
Basis in Classical Physics
Conservation Laws
Stocks and Flows
System State
Partial Differential Equations (PDEs) or Finite Element (FE) Analysis
Ordinary Differential Equations (ODEs)
Systems Analysis Illustrated
SYSTEM PROCESSES
Metabolic Flow Processes
Fluid Flow Processes
Ion Flow Processes
SYSTEM DYNAMICS MODELING
Principles of Stock-and-Flow Modeling
Classical Laws
Systems Pathway Perspective
QUALITATIVE, DISCRETE CAUSAL METHODS
Agent-Based Models
Cause–Effect Inference
Chalkboard Semantics-Based Modeling and Causal Reasoning
Advantages and Limitations of Qualitative Methods
QUANTITATIVE SYSTEMS ANALYSIS
System Dynamic Modeling – Participants, Processes, and Properties
Modeling Continuum Entities – Partial Differential Equations, PDEs
Modeling Systems of Discrete Entities – Ordinary Differential Equations, ODEs
Network Thermodynamics and Bond-Graph Theory
Hybrid Qualitative, Quantitative Modeling
Modularization – A Solution for Biophysical Modeling?
“Goodness of Fit” – The Predictive Value of a Model
Sensitivity Analysis and Model “Optimization”
SUMMARY
Chapter 6 ◾ Ontology of Physics for Biology
OPB – AIMS, SCOPE, AND STATUS
BACKGROUND AND MOTIVATION
OPB – Precursors and Neighbors
OPB ORGANIZATION AND TOP CLASSES
OPB:PHYSICS ANNOTATION ENTITY
OPB:Physics Domain
OPB:Physics Model
OPB:Physics Real Entity
OPB:PHYSICS PROPERTY – OBSERVABLE AND COMPUTABLE
Dynamical Properties
OPB:Dynamical Property Classified by Dynamical Domain
OPB:hasPhysicalDomain Generalizes Physical Rules and Laws
Dynamical Properties Are Attributes of Continuants and Processes
OPB:PHYSICS DEPENDENCY – PHYSICAL LAWS AND CONSTRAINTS
OPB:hasPropertyPlayer Relations
OPB:Calculus Dependency – Temporal and Spatial
OPB:Constitutive Dependency, OPB:Constitutive Property
OPB:Mono-Constitutive Dependency of a Single Participant
OPB:Dual Constitutive Dependency between Two Continuant Participants
OPB:THERMODYNAMIC ENTITY
Thermodynamic Properties and Dependencies
Thermodynamics Is Universal
OPB:PHYSICS PROCESS
“Process” – Ontological Scope, Participation, and Properties
OPB:Physics Process – Observable Process Events
OPB – STATE OF DEVELOPMENT AND FUTURE
Chapter 7 ◾ OPB-Based Semantic Modeling
ANNOTATION OF BIOSIMULATION MODELS: CURRENT PRACTICES
COMPOSITE ANNOTATIONS
THE SEMSIM ARCHITECTURE
Organization of the SemSim Architecture
The SemSim Application Programming Interface (API)
PHYSIOMAP ARCHITECTURE
Representing Processes in PhysioMaps
SEMGEN
QUALITATIVE INFERENCE USING OPB AND SEMSIM MODELS
VISION FOR A SEMANTICALLY-INTEGRATED PHYSIOME
DATA ANNOTATION FOR REUSE
STANDARDIZED ANNOTATIONS FOR MODELING PROJECTS
Chapter 8 ◾ OPB Review and Possibilities
OPB USES AND APPLICATIONS
OPB – QUANTITATIVE FOUNDATIONS; SEMANTIC PERSPECTIVES
BIBLIOGRAPHY
INDEX
Biography
Daniel L. Cook is an Emeritus Professor of Physiology and Biophysics at the University of Washington, Seattle. He graduated (BSME, 1967) from the University of Michigan Mechanical Engineering and worked at the Boeing Airplane Company, first, to manufacture the first 747 airliner, and then to analyze the structural dynamic of the (unbuilt) Boeing supersonic transport (SST). Taking an interest in the emerging field of bioengineering, he earned a Masters Degree in Mechanical Engineering (UW, MSME, 1971) modeling the cellular dynamics of insulin secretion. He then entered the UW's Medical Scientist Training Program (MSTP, 1971) to earn MD and PhD degrees. He has published seminal laboratory and modeling studies of the electrophysiology of insulin secretion, and of auditory sound localization. He is retired and lives in Seattle with his wife.
John H. Gennari is a Professor and Graduate Program Director for Biomedical & Health Informatics (BHI) at the University of Washington. His background is in computer science and artificial intelligence, and was introduced to the field of biomedical informatics in the early 1990s at Stanford University. There, he developed an interest in knowledge representation as applied to biomedical applications, and collaborated with early developers of ontologies. After joining the University of Washington in 2002, he began his collaboration with Max, Dan, and Cornelius Rosse around models of anatomy and physiology. In addition to teaching and leadership roles in BHI, John continues to be active in research, furthering efforts in standards development and reproducibility. John enjoys Seattle and the pacific northwest with his family.
Maxwell L. Neal is a Senior Scientist at Seattle Children’s Research Institute. Since his first exposure to dynamic physiological modeling while working on DARPA’s VSP, Max’s work has focused on applying computational methods to understand various biological systems as well as the development of standards and tools that facilitate systems-level biological modeling. Meeting and collaborating with John and Dan during the VSP established his long-standing interest in semantics-based representations of biosimulation models, which he studied for his Ph.D. at the University of Washington. Since then, he has led the adoption of community-ratified metadata standards for biosimulation and models as well as the development of software for semantics-based biological modeling. He lives in the Seattle area with his wife and son.
