Abstract
As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities—molecules, cells, organs—are well-established, there are no principled ontologies of physical properties—energies, volumes, flow rates—of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration.
Highlights
The biotechnology enterprise, from laboratory bench to bedside, depends on the interpretation of the meaning of data at all structural levels from molecules to whole organisms
For the biomedical domain, we sought a declarative semantics based on the physical meaning of quantities on the premise that it is more critical to know that a model variable or experimental datum is a fluid pressure or tensile stress rather than that it is a scalar or a tensor
As we recognize the value of upper ontologies (UO) for alignment and interoperability, we strive to define OPB classes in a manner consistent with UOs such as Basic Formal Ontology (BFO) [45], General Formal Ontology-Biology (GFO-Bio) [46], and Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) [47]
Summary
The biotechnology enterprise, from laboratory bench to bedside, depends on the interpretation of the meaning of data at all structural levels from molecules to whole organisms. Building on pioneering mathematical modeling methods (e.g., Hodgkin and Huxley [1], Guyton [2]), international research efforts such as the IUPS Physiome [3], the EU Virtual Physiological Human [4], ‘‘systems biology’’ [5], and ‘‘executable biology’’ [6] aim to share data and integrate models across all time scales, spatial scales, and biophysical domains. Such integrative computational efforts are recognizing the value of biomedical ontologies for annotating the biophysical content of their underlying mathematical biosimulation code [7]. We have adopted a classificatory approach proposed by the physicist James Clerk-Maxwell (1831– 1879) in a short note to the London Mathematical Society, ‘‘On the Mathematical Classification of Physical Quantities’’ [31].
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