Journal Article

SBRML: a markup language for associating systems biology data with models

Joseph O. Dada, Irena Spasić, Norman W. Paton and Pedro Mendes

in Bioinformatics

Volume 26, issue 7, pages 932-938
Published in print April 2010 | ISSN: 1367-4803
Published online February 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq069
SBRML: a markup language for associating systems biology data with models

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Motivation: Research in systems biology is carried out through a combination of experiments and models. Several data standards have been adopted for representing models (Systems Biology Markup Language) and various types of relevant experimental data (such as FuGE and those of the Proteomics Standards Initiative). However, until now, there has been no standard way to associate a model and its entities to the corresponding datasets, or vice versa. Such a standard would provide a means to represent computational simulation results as well as to frame experimental data in the context of a particular model. Target applications include model-driven data analysis, parameter estimation, and sharing and archiving model simulations.

Results: We propose the Systems Biology Results Markup Language (SBRML), an XML-based language that associates a model with several datasets. Each dataset is represented as a series of values associated with model variables, and their corresponding parameter values. SBRML provides a flexible way of indexing the results to model parameter values, which supports both spreadsheet-like data and multidimensional data cubes. We present and discuss several examples of SBRML usage in applications such as enzyme kinetics, microarray gene expression and various types of simulation results.

Availability and Implementation: The XML Schema file for SBRML is available at http://www.comp-sys-bio.org/SBRML under the Academic Free License (AFL) v3.0.

Contact: pedro.mendes@manchester.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4641 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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