ORCA: a COBRA toolbox extension for model-driven discovery and analysis

• Published in: Bioinformatics
• Main Authors: Mao, Longfei, Verwoerd, Wynand S.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2013
• Subjects: Computational Mathematics; Computational Theory and Mathematics; Computer Science Applications; Molecular Biology; Biochemistry; Statistics and Probability; Orca
• Online Access: http://dx.doi.org/10.1093/bioinformatics/btt723; https://academic.oup.com/bioinformatics/article-pdf/30/4/584/48917949/bioinformatics_30_4_584.pdf

Abstract Summary: Over past decades, constraint-based modelling has emerged as an important approach to obtain referential information about mechanisms behind biological phenotypes and identify physiological and perturbed metabolic states at genome-scale. However, application of this novel approach to systems biology in biotechnology is still hindered by the functionalities of the existing modelling software. To augment the usability of the constraint-based approach for various use scenarios, we present ORCA, a Matlab package, which extends the scope of established Constraint-Based Reconstruction and Analysis metabolic modelling and includes three unique functionalities: (i) a framework method integrating three analyses of multi-objective optimization, robustness analysis and fractional benefit analysis, (ii) metabolic pathways identification with futile loop elimination and (iii) a dynamic flux balance analysis framework incorporating kinetic constraints. Availability and implementation: ORCA is freely available to academic users and is downloadable from https://sourceforge.net/projects/exorca/; a mini-tutorial is supplied in the package for training purposes as well as a software manual. Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.