We have extended the Ontology of Physics for Biology (OPB) to rep-‐‑ resent the entities and relations of classical thermodynamics. We de-‐‑ scribe key subclasses of OPB:Thermodynamic entity such as OPB: Thermodynamic property, and OPB:Thermodynamic dependency in the context of the OPB’s overall representational schema. We are motivat-‐‑ ed by practical utility of energy bond-‐‑graph theory, a thermodynamics-‐‑ based formalism used in the domain of system dynamical modeling and analysis of biological physical processes. We also intend OPB to extend available upper biomedical ontologies to encompass entities and theories of classical physics and thermodynamics.
The Ontology of Physics for Biology
Our SemGen application1 uses OPB semantics to derive
and analyze SemSim models (semantic simulation) to input,
parse, and annotate biosimulation model code. Furthermore,
SemGen can decompose SemSim models into reuseable
fragments, merge the fragments as a new SemSim model
and export new computational model code . A SemSim
model is a light-weight OWL ontology that annotates each
variable as an instance of an OPB:Physical property and
each equation as an instance of OPB:Physical dependency
to create a “property dependency graph” (OPB:Property
dependency graph). We have recently applied SWRL rules
to SemSim models and OPB to infer qualitative changes of
model variables on other property values in the model
OPB parses system dynamical abstractions into 4
highlevel classes. First, OPB:Dynamical entities are
energybearing physical continuants such as portions of fluid,
chemical, charge. Second, OPB:Dynamical processes are
flows of material, charge, etc. as in chemical reactions, fluid
flows, etc. Third, OPB:Physical properties
To formally represent and constrain models of such
dynamical phenomena, we have extended OPB to explicitly
represent thermodynamic entities, properties, and
dependencies. Whereas properly derived system dynamical models
will constrain models to the universal rules of
thermodynamics (e.g., conservation of energy, in particular), such
constraints are only implicit in model equations. To
explicitly satisfy both dynamical and thermodynamic laws and
constraints, thermodynamics-based energy bond graph
modeling was first described for biological systems (Perelson,
1975), adapted to engineering practice
We have extended OPB to represent the entities and principles of classical thermodynamics in support of thermodynamic-based computational modeling as well as to extend the scant representation of physical and thermodynamical concepts in prevailing upper biomedical ontologies.
In parallel to OPB system dynamical classes that represent stocks/flow of material, charge, etc., OPB thermodynamic classes represent stocks/flows of energy and entropy (Figure 1). Thus, an instance of OPB:Mechanical solid has (via OPB:hasThermodynamicEntity) a portion of OPB:Solid potential energy if stretched or compressed and/or a portion of OPB:Solid kinetic energy if in motion. 2.2
Thermodynamical entities have OPB:Thermodynamical properties: (1) rate properties (e.g., OPB:Energy flow rate, OPB:Entropy flow rate), (2) state properties (e.g., OPB:Energy amount, OPB:Entropy amount) and (3) constitutive properties (e.g., OPB:Thermal capacity, OPB:Thermal conductivity).
OPB:Thermodynamical dependencies (Fig. 2) define OPB:Thermodynamical properties in terms of other such properties or in terms of OPB:Dynamical properties (e.g., fluid volume or pressure).
Figure 3 is an overview of OPB classes showing the scope and depth of the OPB’s representation of physics of biological processes. We have aimed to, first, extend upper ontologies to encompass entities and relations of physics as used by bioengineers and biophysicists, and now, to encompass thermodynamic entities and laws that govern biological processes. OPB is a reference ontology of biophysics that extends available "upper ontologies" (e.g, BFO, GFO), complements domain ontologies such as FMA, GO, ChEBI, and provides a computational resource for annotating biophysical models and datasets for reuse and integration.
The authors thank Cornelius Rosse and Peter Hunter. This research was partially supported by the National Institutes of Health, grant R01LM011969.