Rafael Carreira1,2,3, Pedro Evangelista4,5, Paulo Maia6,7, Paulo Vilaça8,9, Marcellinus Pont10, Jean-François Tomb11, Isabel Rocha12, Miguel Rocha13. 1. Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal. rafaelcc@di.uminho.pt. 2. CCTC, University of Minho, Campus de Gualtar, Braga, Portugal. rafaelcc@di.uminho.pt. 3. SilicoLife, Lda, Rua do Canastreiro 15, Braga, Portugal. rafaelcc@di.uminho.pt. 4. Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal. pevangelista@silicolife.com. 5. SilicoLife, Lda, Rua do Canastreiro 15, Braga, Portugal. pevangelista@silicolife.com. 6. Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal. pmaia@silicolife.com. 7. SilicoLife, Lda, Rua do Canastreiro 15, Braga, Portugal. pmaia@silicolife.com. 8. CCTC, University of Minho, Campus de Gualtar, Braga, Portugal. pvilaca@silicolife.com. 9. SilicoLife, Lda, Rua do Canastreiro 15, Braga, Portugal. pvilaca@silicolife.com. 10. E.I. DuPont De Nemours & Co., Inc, Wilmington, DE, USA. marcellinus.pont@usa.dupont.com. 11. E.I. DuPont De Nemours & Co., Inc, Wilmington, DE, USA. jean-francois.tomb@usa.dupont.com. 12. Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal. irocha@deb.uminho.pt. 13. Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal. mrocha@di.uminho.pt.
Abstract
BACKGROUND: Flux analysis methods lie at the core of Metabolic Engineering (ME), providing methods for phenotype simulation that allow the determination of flux distributions under different conditions. Although many constraint-based modeling software tools have been developed and published, none provides a free user-friendly application that makes available the full portfolio of flux analysis methods. RESULTS: This work presents Constraint-based Flux Analysis (CBFA), an open-source software application for flux analysis in metabolic models that implements several methods for phenotype prediction, allowing users to define constraints associated with measured fluxes and/or flux ratios, together with environmental conditions (e.g. media) and reaction/gene knockouts. CBFA identifies the set of applicable methods based on the constraints defined from user inputs, encompassing algebraic and constraint-based simulation methods. The integration of CBFA within the OptFlux framework for ME enables the utilization of different model formats and standards and the integration with complementary methods for phenotype simulation and visualization of results. CONCLUSIONS: A general-purpose and flexible application is proposed that is independent of the origin of the constraints defined for a given simulation. The aim is to provide a simple to use software tool focused on the application of several flux prediction methods.
BACKGROUND: Flux analysis methods lie at the core of Metabolic Engineering (ME), providing methods for phenotype simulation that allow the determination of flux distributions under different conditions. Although many constraint-based modeling software tools have been developed and published, none provides a free user-friendly application that makes available the full portfolio of flux analysis methods. RESULTS: This work presents Constraint-based Flux Analysis (CBFA), an open-source software application for flux analysis in metabolic models that implements several methods for phenotype prediction, allowing users to define constraints associated with measured fluxes and/or flux ratios, together with environmental conditions (e.g. media) and reaction/gene knockouts. CBFA identifies the set of applicable methods based on the constraints defined from user inputs, encompassing algebraic and constraint-based simulation methods. The integration of CBFA within the OptFlux framework for ME enables the utilization of different model formats and standards and the integration with complementary methods for phenotype simulation and visualization of results. CONCLUSIONS: A general-purpose and flexible application is proposed that is independent of the origin of the constraints defined for a given simulation. The aim is to provide a simple to use software tool focused on the application of several flux prediction methods.
Authors: Jan Schellenberger; Richard Que; Ronan M T Fleming; Ines Thiele; Jeffrey D Orth; Adam M Feist; Daniel C Zielinski; Aarash Bordbar; Nathan E Lewis; Sorena Rahmanian; Joseph Kang; Daniel R Hyduke; Bernhard Ø Palsson Journal: Nat Protoc Date: 2011-08-04 Impact factor: 13.491
Authors: Nathan E Lewis; Kim K Hixson; Tom M Conrad; Joshua A Lerman; Pep Charusanti; Ashoka D Polpitiya; Joshua N Adkins; Gunnar Schramm; Samuel O Purvine; Daniel Lopez-Ferrer; Karl K Weitz; Roland Eils; Rainer König; Richard D Smith; Bernhard Ø Palsson Journal: Mol Syst Biol Date: 2010-07 Impact factor: 11.429
Authors: Markus Heinonen; Maria Osmala; Henrik Mannerström; Janne Wallenius; Samuel Kaski; Juho Rousu; Harri Lähdesmäki Journal: Bioinformatics Date: 2019-07-15 Impact factor: 6.937