Abstract
BackgroundFlux 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.ResultsThis 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.ConclusionsA 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0123-1) contains supplementary material, which is available to authorized users.
Highlights
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
Current biochemical knowledge and the information collected from the annotation of genome sequencing projects allowed the development of genome-scale metabolic models (GSMMs), supporting the simulation of the metabolic phenotypes
System configuration Starting with a metabolic model that can be loaded in different formats (e.g. Systems Biology Markup Language (SBML) or CSV files), the user can configure inputs to flux analysis methods, including environmental and genetic conditions, measured or otherwise known fluxes and/or flux ratios given as expressions in the form: X Xninj1⁄41⁄411κκijυυji 1⁄4 τ where κi, κj ∈ R and τ ∈ R\{0} are user-defined real numbers and vi and vj are fluxes
Summary
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. Through the use of a metabolic model, taking into account stoichiometry, reaction reversibility, and quasisteady-state assumptions, linear constraints over the values of intracellular fluxes can be established. Simulation approaches based on linear/quadratic programming (LP/QP) optimization methods are used to calculate flux values. This is the case with the well-known Flux Balance Analysis (FBA) method [8], where an optimization problem is formulated to optimize a defined objective function, typically the maximization of growth rate as defined by an artificial biomass flux [9]
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