Open AccessControlled vocabularies and semantics in systems biology
Author(s) -
Courtot Mélanie,
Juty Nick,
Knüpfer Christian,
Waltemath Dagmar,
Zhukova Anna,
Dräger Andreas,
Dumontier Michel,
Finney Andrew,
Golebiewski Martin,
Hastings Janna,
Hoops Stefan,
Keating Sarah,
Kell Douglas B,
Kerrien Samuel,
Lawson James,
Lister Allyson,
Lu James,
Machne Rainer,
Mendes Pedro,
Pocock Matthew,
Rodriguez Nicolas,
Villeger Alice,
Wilkinson Darren J,
Wimalaratne Sarala,
Laibe Camille,
Hucka Michael,
Le Novère Nicolas
Publication year - 2011
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.1038/msb.2011.77
Subject(s) - ontology , terminology , semantics (computer science) , computer science , systems biology , reuse , open biomedical ontologies , biology , data science , semantic web , computational biology , information retrieval , ontology based data integration , programming language , suggested upper merged ontology , ecology , philosophy , linguistics , epistemology
The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.