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Editorial
. 2016 Jul 11;10(1):46.
doi: 10.1186/s12918-016-0284-1.

PFA toolbox: a MATLAB tool for Metabolic Flux Analysis

Yeimy Morales  1 , Gabriel Bosque  2 , Josep Vehí  3 , Jesús Picó  2 , Francisco Llaneras  3
Affiliations
Editorial

PFA toolbox: a MATLAB tool for Metabolic Flux Analysis

Yeimy Morales et al. BMC Syst Biol. .

Abstract

Background: Metabolic Flux Analysis (MFA) is a methodology that has been successfully applied to estimate metabolic fluxes in living cells. However, traditional frameworks based on this approach have some limitations, particularly when measurements are scarce and imprecise. This is very common in industrial environments. The PFA Toolbox can be used to face those scenarios.

Results: Here we present the PFA (Possibilistic Flux Analysis) Toolbox for MATLAB, which simplifies the use of Interval and Possibilistic Metabolic Flux Analysis. The main features of the PFA Toolbox are the following: (a) It provides reliable MFA estimations in scenarios where only a few fluxes can be measured or those available are imprecise. (b) It provides tools to easily plot the results as interval estimates or flux distributions. (c) It is composed of simple functions that MATLAB users can apply in flexible ways. (d) It includes a Graphical User Interface (GUI), which provides a visual representation of the measurements and their uncertainty. (e) It can use stoichiometric models in COBRA format. In addition, the PFA Toolbox includes a User's Guide with a thorough description of its functions and several examples.

Conclusions: The PFA Toolbox for MATLAB is a freely available Toolbox that is able to perform Interval and Possibilistic MFA estimations.

Keywords: Constraint-based modelling; Interval MFA; Metabolic Flux Analysis; Possibilistic MFA.

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Figures

Fig. 1
Fig. 1
Protocol to use the PFA Toolbox. A step by step to use the PFA Toolbox. Protocol is the same to solve the MFA problems with Interval and possibilistic MFA. Possibilistic has two additional steps, which are optional, a Graphical User Interface (GUI) to represent graphically the measures in possibilistic terms and a function to check if the measures and their uncertainties are well-defined
Fig. 2
Fig. 2
PFA Toolbox methodology to solve example of flux estimation under data scarcity. a Upper panel present a simple metabolic network. Metabolites are in capital letters, each vj represent a flux and the double arrows indicate a reversible reaction. b The step-by-step procedure follow to solve the MFA problem where only two measures are known. c Right panel shows the MATLAB code used to perform the computations
Fig. 3
Fig. 3
Flux estimation. Estimations for every flux were obtained with the PFA Toolbox. a Three interval estimates are given, for maximum conditional possibility (box), possibility of 0.8 (black line), and 0.5 (gray line). b Possibility distributions are depicted with solid lines and dashed lines represent measured values
Fig. 4
Fig. 4
Growth estimations with possibilistic MFA for P. pastoris and E. coli. a Example with six P. pastoris experiments. b Example with E. coli experiments. In both cases, three interval estimates are represented, for conditional possibilities equal to 0.99 (box), 0.5 (bar) and 0.1 (line). The crosses represent the actual experimental values
See this image and copyright information in PMC

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