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Open Access Research

Ontology of physics for biology: representing physical dependencies as a basis for biological processes

Daniel L Cook1*, Maxwell L Neal3, Fred L Bookstein4 and John H Gennari2

Author Affiliations

1 Department of Physiology & Biophysics, University of Washington, Seattle 98195, USA

2 Department of Biomedical & Health Informatics, University of Washington, Seattle 98195, USA

3 Department of Bioengineering, University of Washington, Seattle 98195, USA

4 Department of Statistics, University of Washington, Seattle 98195, USA

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Journal of Biomedical Semantics 2013, 4:41  doi:10.1186/2041-1480-4-41

Published: 2 December 2013

Abstract

Background

In prior work, we presented the Ontology of Physics for Biology (OPB) as a computational ontology for use in the annotation and representations of biophysical knowledge encoded in repositories of physics-based biosimulation models. We introduced OPB:Physical entity and OPB:Physical property classes that extend available spatiotemporal representations of physical entities and processes to explicitly represent the thermodynamics and dynamics of physiological processes. Our utilitarian, long-term aim is to develop computational tools for creating and querying formalized physiological knowledge for use by multiscale “physiome” projects such as the EU’s Virtual Physiological Human (VPH) and NIH’s Virtual Physiological Rat (VPR).

Results

Here we describe the OPB:Physical dependency taxonomy of classes that represent of the laws of classical physics that are the “rules” by which physical properties of physical entities change during occurrences of physical processes. For example, the fluid analog of Ohm’s law (as for electric currents) is used to describe how a blood flow rate depends on a blood pressure gradient. Hooke’s law (as in elastic deformations of springs) is used to describe how an increase in vascular volume increases blood pressure. We classify such dependencies according to the flow, transformation, and storage of thermodynamic energy that occurs during processes governed by the dependencies.

Conclusions

We have developed the OPB and annotation methods to represent the meaning—the biophysical semantics—of the mathematical statements of physiological analysis and the biophysical content of models and datasets. Here we describe and discuss our approach to an ontological representation of physical laws (as dependencies) and properties as encoded for the mathematical analysis of biophysical processes.