SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology

Summary, in English

Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI's use of standard data formats.

• Author: Richard Adams; Allan Clark; Azusa Yamaguchi; Neil Hanlon; Nikos Tsorman; Shakir Ali; Galina Lebedeva; Alexey Goltsov; Anatoly Sorokin; Ozgur E. Akman; Carl Troein; Andrew J. Millar; Igor Goryanin; Stephen Gilmore
• Publishing year: 2013
• Language: English
• Pages: 664-665
• Publication/Series: Bioinformatics
• Volume: 29
• Issue: 5
• Links: http://dx.doi.org/10.1093/bioinformatics/btt023
• Document type: Journal article
• Publisher: Oxford University Press
• Topic: Bioinformatics and Systems Biology
• Status: Published
• ISBN/ISSN/Other: ISSN: 1367-4803