Category:Virtual biology ontologies

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Ontologies are hierarchically structured vocabularies of terms and relationships that are clearly defined and designed to represent and communicate information about a particular scientific domain.

Virtual biology uses several biomedical ontologies for unambiguous, systematic descriptions of physiological, biological, chemical and physics-based entities and processes as well as their interrelations [1].

Key elements in annotating models with ontology terms include [1, 2]:

  • associating codewords in the model with appropriate unambiguous ontology identifiers;
  • specifying components and subcomponents in the model utilizing the hierarchical structure within an ontology;
  • linking the model and its components and subcomponents to supporting measured experimental data.

The use of multiple ontologies for defining components and subcomponents of models could allow them to be compared and integrated to form composite models in an automated manner [2].

Standardizing biological information with organized vocabularies and ontologies already has proven to be valuable in formally defining components of models and representations of complex systems[1, 2]. For example, describing multiscale processes in mouse development mathematical models using a combination of GO and Cell Type Ontology terms has been shown to be effective to provide clear definitions of function and to allow comparison of function under different conditions [3].

The table below contains brief description of these ontologies.

Ontology, references Brief description, URL Used in projects
GO - Gene Ontology[4] Describes gene function through properties of proteins and includes hierarchical information in the three

domains of cellular location, molecular functions, and biological processes. http://www.

virtual rat[2]
FMA - Foundational Model of Anatomy [5] Provides a hierarchical, structured knowledge base of human anatomy.

virtual rat[2]
OPB - Ontology of Physics for Biology [6] Designed for annotating physical properties encoded in biomedical datasets and computational models.

virtual rat[2]
SBO - Systems Biology Ontology[1] Is a consensus ontological framework that has been developed to identify and annotate model components including component types, component roles, physical entities and their associated mathematical expressions. Main focus of the SBO is on chemical reaction systems.

KiSAO - Kinetic Simulation Algorithm Ontology [1] Supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships.

TEDDY - Terminology for the Description of Dynamics [1] Describe the form of the simulation results, which then can be used to identify experimental results in the validation step of the model.

The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships.


  1. Courtot M, Juty N, Knüpfer C, Waltemath D, Zhukova A, Dräger A, Dumontier M, Finney A, Golebiewski M, Hastings J, Hoops S, Keating S, Kell DB, Kerrien S, Lawson J, Lister A, Lu J, Machne R, Mendes P, Pocock M, Rodriguez N, Villeger A, Wilkinson DJ, Wimalaratne S, Laibe C, Hucka M, and Le Novère N. Controlled vocabularies and semantics in systems biology. Mol Syst Biol. 2011 Oct 25;7:543. DOI:10.1038/msb.2011.77 | PubMed ID:22027554 | HubMed [Courtot2011]
  2. Beard DA, Neal ML, Tabesh-Saleki N, Thompson CT, Bassingthwaighte JB, Shimoyama M, and Carlson BE. Multiscale modeling and data integration in the virtual physiological rat project. Ann Biomed Eng. 2012 Nov;40(11):2365-78. DOI:10.1007/s10439-012-0611-7 | PubMed ID:22805979 | HubMed [Beard2013]
  3. Bard J. Systems developmental biology: the use of ontologies in annotating models and in identifying gene function within and across species. Mamm Genome. 2007 Jul;18(6-7):402-11. DOI:10.1007/s00335-007-9027-3 | PubMed ID:17566825 | HubMed [Bard2007]
  4. Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin GM, Blake JA, Bult C, Dolan M, Drabkin H, Eppig JT, Hill DP, Ni L, Ringwald M, Balakrishnan R, Cherry JM, Christie KR, Costanzo MC, Dwight SS, Engel S, Fisk DG, Hirschman JE, Hong EL, Nash RS, Sethuraman A, Theesfeld CL, Botstein D, Dolinski K, Feierbach B, Berardini T, Mundodi S, Rhee SY, Apweiler R, Barrell D, Camon E, Dimmer E, Lee V, Chisholm R, Gaudet P, Kibbe W, Kishore R, Schwarz EM, Sternberg P, Gwinn M, Hannick L, Wortman J, Berriman M, Wood V, de la Cruz N, Tonellato P, Jaiswal P, Seigfried T, White R, and Gene Ontology Consortium.. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D258-61. DOI:10.1093/nar/gkh036 | PubMed ID:14681407 | HubMed [Harris2004]
  5. Rosse C and Mejino JL Jr. A reference ontology for biomedical informatics: the Foundational Model of Anatomy. J Biomed Inform. 2003 Dec;36(6):478-500. DOI:10.1016/j.jbi.2003.11.007 | PubMed ID:14759820 | HubMed [Rosse2003]
  6. Cook DL, Bookstein FL, and Gennari JH. Physical properties of biological entities: an introduction to the ontology of physics for biology. PLoS One. 2011;6(12):e28708. DOI:10.1371/journal.pone.0028708 | PubMed ID:22216106 | HubMed [Cook2011]
All Medline abstracts: PubMed | HubMed

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