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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.

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Related in: MedlinePlus

Visualization of Flux Analysis results: a) The results of the flux calculation for Saccharomyces cerevisiae are illustrated. The flux values were obtained through least squares, since the configured system was determined. The thickness of the arrows is proportional to the flux value of the corresponding reactions, after overlaying the fluxes over the network layout. Thin light grey arrows represent reactions with no flux value. b) The central carbon metabolism of Escherichia coli is shown with a comparison between pFBA-based simulations before and after adding metabolic flux ratio constraints. Here, grey arrows indicate reactions where there is no flux in both simulations, while red and green arrows represent reactions for which the simulations with and without flux ratios, respectively, returned flux values. The darker the arrows are, the nearer the fluxes in both simulations.
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Fig4: Visualization of Flux Analysis results: a) The results of the flux calculation for Saccharomyces cerevisiae are illustrated. The flux values were obtained through least squares, since the configured system was determined. The thickness of the arrows is proportional to the flux value of the corresponding reactions, after overlaying the fluxes over the network layout. Thin light grey arrows represent reactions with no flux value. b) The central carbon metabolism of Escherichia coli is shown with a comparison between pFBA-based simulations before and after adding metabolic flux ratio constraints. Here, grey arrows indicate reactions where there is no flux in both simulations, while red and green arrows represent reactions for which the simulations with and without flux ratios, respectively, returned flux values. The darker the arrows are, the nearer the fluxes in both simulations.

Mentions: As an illustration of the previous results, in Figure 4, the distribution of the central carbon metabolism fluxes in both case studies is illustrated by overlaying fluxes over the network topology. This figure also serves as an example of the visualization capabilities of the tool.Figure 4


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)

Visualization of Flux Analysis results: a) The results of the flux calculation for Saccharomyces cerevisiae are illustrated. The flux values were obtained through least squares, since the configured system was determined. The thickness of the arrows is proportional to the flux value of the corresponding reactions, after overlaying the fluxes over the network layout. Thin light grey arrows represent reactions with no flux value. b) The central carbon metabolism of Escherichia coli is shown with a comparison between pFBA-based simulations before and after adding metabolic flux ratio constraints. Here, grey arrows indicate reactions where there is no flux in both simulations, while red and green arrows represent reactions for which the simulations with and without flux ratios, respectively, returned flux values. The darker the arrows are, the nearer the fluxes in both simulations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Visualization of Flux Analysis results: a) The results of the flux calculation for Saccharomyces cerevisiae are illustrated. The flux values were obtained through least squares, since the configured system was determined. The thickness of the arrows is proportional to the flux value of the corresponding reactions, after overlaying the fluxes over the network layout. Thin light grey arrows represent reactions with no flux value. b) The central carbon metabolism of Escherichia coli is shown with a comparison between pFBA-based simulations before and after adding metabolic flux ratio constraints. Here, grey arrows indicate reactions where there is no flux in both simulations, while red and green arrows represent reactions for which the simulations with and without flux ratios, respectively, returned flux values. The darker the arrows are, the nearer the fluxes in both simulations.
Mentions: As an illustration of the previous results, in Figure 4, the distribution of the central carbon metabolism fluxes in both case studies is illustrated by overlaying fluxes over the network topology. This figure also serves as an example of the visualization capabilities of the tool.Figure 4

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
Related in: MedlinePlus