Showing papers by "Nick Juty published in 2011"
European Bioinformatics Institute1, University of Jena2, University of Rostock3, University of Tübingen4, Carleton University5, Ansys6, Virginia Bioinformatics Institute7, University of Manchester8, University of Auckland9, Newcastle University10, ETH Zurich11, University of Vienna12, California Institute of Technology13
TL;DR: Three ontologies created specifically to address the needs of the systems biology community are described, including the Systems Biology Ontology, which provides semantic information about the model components, and the Kinetic Simulation Algorithm Ontology and the Terminology for the Description of Dynamics, which categorizes dynamical features of the simulation results and general systems behavior.
Abstract: 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.
298 citations
European Bioinformatics Institute1, University of Jena2, University of Rostock3, University of Tübingen4, Carleton University5, Ansys6, Virginia Bioinformatics Institute7, University of Manchester8, University of Auckland9, Newcastle University10, ETH Zurich11, University of Vienna12, California Institute of Technology13
TL;DR: The Systems Biology Ontology (SBO) as mentioned in this paper provides semantic information about the model components, and the Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior.
Abstract: 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.
17 citations
TL;DR: This proposal extends the current Core annotation specification to provide support for a richer set of semantic annotations while adhering more closely to the existing specification of Resource Description Framework (RDF).
Abstract: The annotation of Systems Biology Markup Language (SBML) models with semantic terms has been supported for a number of years. The prevalence of such annotated models is growing, with repositories such as Biomodels.net and an increasing number of software tools supporting and encouraging their use and development.
With the increasing use of semantic annotations in the context of systems biology modeling has come the realization that the current Core SBML specification defining their use contains limitations that reduce the scope of metadata that can be captured in such models.
SBML Level 3 provides the facility to propose and develop optional extensions to the Core specification. One such extension is described here, with an initial proposal of an Annotation package.
This proposal extends the current Core annotation specification to provide support for a richer set of semantic annotations while adhering more closely to the existing specification of Resource Description Framework (RDF).
4 citations
TL;DR: The Kinetic Simulation Algorithm Ontology (KiSAO) was developed to address this issue by describing existing algorithms and their inter-relationships through their characteristics and parameters to allow simulation software to automatically choose the best algorithm available to perform a simulation.
Abstract: To enable the accurate and repeatable execution of a computational simulation task, it is important to identify both the algorithm used and the initial setup. These minimum information requirements are described by the MIASE guidelines. Since the details of some algorithms are not always publicly available, and many are implemented only in a limited number of simulation tools, it is crucial to identify alternative algorithms with similar characteristics that may be used to provide comparable results in an equivalent simulation experiment. The Kinetic Simulation Algorithm Ontology (KiSAO) was developed to address this issue by describing existing algorithms and their inter-relationships through their characteristics and parameters. The use of KiSAO in conjunction with simulation descriptions, such as SED-ML, will allow simulation software to automatically choose the best algorithm available to perform a simulation. The availability of algorithm parameters, together with their type may permit the automatic generation of user-interfaces to configure simulators. To enable making queries to KiSAO programmaticaly, from simulation experiment description editors and simulation tools, a java library libKiSAO was implemented.
3 citations
TL;DR: The MIRIAM Registry as mentioned in this paper is a foundation layer database upon which persistent, unambiguous and perennial identifiers of data can be built, and it is used to define a set of URI identifiers.
Abstract: We describe the MIRIAM Registry, which forms a foundation layer database upon which persistent, unambiguous and perennial identifiers of data can be built. We describe the existing MIRIAM URI identifiers, and the newly minted resolvable, parallel forms of these identifiers which use Identifiers.org URLs.
1 citations