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CBFA: phenotype prediction integrating metabolic models with constraints derived from experimental data.

Carreira R, Evangelista P, Maia P, Vilaça P, Pont M, Tomb JF, Rocha I, Rocha M - BMC Syst Biol (2014)

Bottom Line: 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.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.The aim is to provide a simple to use software tool focused on the application of several flux prediction methods.

View Article: PubMed Central - PubMed

Affiliation: Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal. rafaelcc@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.

Show MeSH
Screenshots of CBFA: a) Load model – the first step is to load a metabolic model and a new project will be created and made available on the clipboard of the application. Afterwards, it is possible to visualize their information through the available views; b) Configure constraints – different type of constraints can be configured through the use of graphical interfaces that enable to set the parameters to create the datatypes to be used on further operations; c) Perform flux analysis/knockout flux analysis – the user can select different constraints to perform several flux analysis methods using a metabolic model. It is also possible to configure a set of knockouts and perform flux analysis over the mutant with the same constraints; d) Visualization – the results of the operations create/update datatypes on the clipboard, and the output can always be checked through the views that are defined for the datatypes.
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Fig3: Screenshots of CBFA: a) Load model – the first step is to load a metabolic model and a new project will be created and made available on the clipboard of the application. Afterwards, it is possible to visualize their information through the available views; b) Configure constraints – different type of constraints can be configured through the use of graphical interfaces that enable to set the parameters to create the datatypes to be used on further operations; c) Perform flux analysis/knockout flux analysis – the user can select different constraints to perform several flux analysis methods using a metabolic model. It is also possible to configure a set of knockouts and perform flux analysis over the mutant with the same constraints; d) Visualization – the results of the operations create/update datatypes on the clipboard, and the output can always be checked through the views that are defined for the datatypes.

Mentions: These concepts are used to build the application layer and to construct graphical user interfaces, which intermediate between the user and all the core methods implemented. In Figure 2, the architecture of the application is illustrated showing the main datatypes, views and operations, as well as the relationship with the core classes utilized in the implementation. A complementary view is given by Figure 3, which provides snapshots of some views and interfaces of the operations invoked when performing simulations and constraints configuration, showing a typical workflow when working with CBFA.Figure 2


CBFA: phenotype prediction integrating metabolic models with constraints derived from experimental data.

Carreira R, Evangelista P, Maia P, Vilaça P, Pont M, Tomb JF, Rocha I, Rocha M - BMC Syst Biol (2014)

Screenshots of CBFA: a) Load model – the first step is to load a metabolic model and a new project will be created and made available on the clipboard of the application. Afterwards, it is possible to visualize their information through the available views; b) Configure constraints – different type of constraints can be configured through the use of graphical interfaces that enable to set the parameters to create the datatypes to be used on further operations; c) Perform flux analysis/knockout flux analysis – the user can select different constraints to perform several flux analysis methods using a metabolic model. It is also possible to configure a set of knockouts and perform flux analysis over the mutant with the same constraints; d) Visualization – the results of the operations create/update datatypes on the clipboard, and the output can always be checked through the views that are defined for the datatypes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4263207&req=5

Fig3: Screenshots of CBFA: a) Load model – the first step is to load a metabolic model and a new project will be created and made available on the clipboard of the application. Afterwards, it is possible to visualize their information through the available views; b) Configure constraints – different type of constraints can be configured through the use of graphical interfaces that enable to set the parameters to create the datatypes to be used on further operations; c) Perform flux analysis/knockout flux analysis – the user can select different constraints to perform several flux analysis methods using a metabolic model. It is also possible to configure a set of knockouts and perform flux analysis over the mutant with the same constraints; d) Visualization – the results of the operations create/update datatypes on the clipboard, and the output can always be checked through the views that are defined for the datatypes.
Mentions: These concepts are used to build the application layer and to construct graphical user interfaces, which intermediate between the user and all the core methods implemented. In Figure 2, the architecture of the application is illustrated showing the main datatypes, views and operations, as well as the relationship with the core classes utilized in the implementation. A complementary view is given by Figure 3, which provides snapshots of some views and interfaces of the operations invoked when performing simulations and constraints configuration, showing a typical workflow when working with CBFA.Figure 2

Bottom Line: 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.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.The aim is to provide a simple to use software tool focused on the application of several flux prediction methods.

View Article: PubMed Central - PubMed

Affiliation: Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal. rafaelcc@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.

Show MeSH