SBMLmod: a Python-based web application and web service for efficient data integration and model simulation

BMC Bioinformatics

Software

Pål Puntervoll¹  Ines Heiland²  Mathias Bockwoldt²  Anne-Kristin Stavrum³  Sascha Schäuble⁴ 

[1] Centre for Applied Biotechnology, Uni Research Environment, Bergen, Norway;Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway;Department of Informatics, University of Bergen, Bergen, Norway;Jena University Language & Information Engineering (JULIE) Lab, Friedrich-Schiller-University Jena, Jena, Germany;

关键词: Web application;    Web service;    Data integration;    Model simulation;   

DOI  :  10.1186/s12859-017-1722-9

 received in 2017-02-10, accepted in 2017-06-09,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundSystems Biology Markup Language (SBML) is the standard model representation and description language in systems biology. Enriching and analysing systems biology models by integrating the multitude of available data, increases the predictive power of these models. This may be a daunting task, which commonly requires bioinformatic competence and scripting.ResultsWe present SBMLmod, a Python-based web application and service, that automates integration of high throughput data into SBML models. Subsequent steady state analysis is readily accessible via the web service COPASIWS. We illustrate the utility of SBMLmod by integrating gene expression data from different healthy tissues as well as from a cancer dataset into a previously published model of mammalian tryptophan metabolism.ConclusionSBMLmod is a user-friendly platform for model modification and simulation. The web application is available at http://sbmlmod.uit.no, whereas the WSDL definition file for the web service is accessible via http://sbmlmod.uit.no/SBMLmod.wsdl. Furthermore, the entire package can be downloaded from https://github.com/MolecularBioinformatics/sbml-mod-ws. We envision that SBMLmod will make automated model modification and simulation available to a broader research community.

【 授权许可】

CC BY   
© The Author(s) 2017