processes
Article
FluxVisualizer, a Software to Visualize Fluxes
through Metabolic Networks
Tim Daniel Rose 1,2,3 and Jean-Pierre Mazat 2,3, *
1
Institute for Quantitative and Theoretical Biology, Heinrich-Heine-University, 40225 Duesseldorf, Germany;
tim@rose-4-you.de
2
IBGC-CNRS UMR 5095, 1 rue Camille Saint Saens, CS 61390, 33077 Bordeaux-cedex, France
3
University of Bordeaux France, 33076 Bordeaux-cedex, France
* Correspondence: jean-pierre.mazat@u-bordeaux.fr; Tel.: +33-556-999-041
Received: 2 March 2018; Accepted: 17 April 2018; Published: 24 April 2018
Abstract: FluxVisualizer (Version 1.0, 2017, freely available at https://fluxvisualizer.ibgc.cnrs.fr)isa
software to visualize fluxes values on a scalable vector graphic (SVG) representation of a metabolic
network by colouring or increasing the width of reaction arrows of the SVG file. FluxVisualizer does
not aim to draw metabolic networks but to use a customer’s SVG file allowing him to exploit his
representation standards with a minimum of constraints. FluxVisualizer is especially suitable for
small to medium size metabolic networks, where a visual representation of the fluxes makes sense.
The flux distribution can either be an elementary flux mode (EFM), a flux balance analysis (FBA)
result or any other flux distribution. It allows the automatic visualization of a series of pathways of
the same network as is needed for a set of EFMs. The software is coded in python3 and provides a
graphical user interface (GUI) and an application programming interface (API). All functionalities
of the program can be used from the API and the GUI and allows advanced users to add their own
functionalities. The software is able to work with various formats of flux distributions (Metatool,
CellNetAnalyzer, COPASI and FAME export files) as well as with Excel files. This simple software
can save a lot of time when evaluating fluxes simulations on a metabolic network.
Keywords: metabolic network visualization; metabolic modelling; elementary flux modes visualization;
flux balance analysis
1. Introduction
The study of genome-scale metabolic models has grown strongly in recent years. This has
stimulated the development of visualization software of large models of metabolism [1–3] for
reviews. At the same time, new methods of studying metabolic fluxes have emerged which lead
to the enumeration of EFMs, Flux Balance Analysis (FBA) [4,5] for reviews alongside more traditional
methods such as dynamical systems and Metabolic Control Analysis [6–8] using the rate functions
of the metabolic steps [9,10]. However only FBA can be applied to the greatest genome-scale
models. As a matter of fact, the number of EFMs highly increases and it is not possible to calculate
them. Furthermore, the rate equations are not entirely known at the level of genome scale model,
particularly the number of enzymes. Consequently, it is not possible to derive a pertinent dynamical
system describing the behaviour of a genome-scale model in a physiological context. For these
reasons, reduced metabolic models, or core models, are often derived to study particular problems
or for a manoeuvrable approach to metabolism [11–13]. In this type of approach drawing a reduced
metabolic network plays an essential role. It usually summarizes the results or hypotheses of
the authors in the form of pathways of different colours or of different sizes according to the
flux values. Many software already exist for automatically generating flux maps for a metabolic
Processes 2018, 6, 39; doi:10.3390/pr6050039
www.mdpi.com/journal/processes
144
Article
FluxVisualizer, a Software to Visualize Fluxes
through Metabolic Networks
Tim Daniel Rose 1,2,3 and Jean-Pierre Mazat 2,3, *
1
Institute for Quantitative and Theoretical Biology, Heinrich-Heine-University, 40225 Duesseldorf, Germany;
tim@rose-4-you.de
2
IBGC-CNRS UMR 5095, 1 rue Camille Saint Saens, CS 61390, 33077 Bordeaux-cedex, France
3
University of Bordeaux France, 33076 Bordeaux-cedex, France
* Correspondence: jean-pierre.mazat@u-bordeaux.fr; Tel.: +33-556-999-041
Received: 2 March 2018; Accepted: 17 April 2018; Published: 24 April 2018
Abstract: FluxVisualizer (Version 1.0, 2017, freely available at https://fluxvisualizer.ibgc.cnrs.fr)isa
software to visualize fluxes values on a scalable vector graphic (SVG) representation of a metabolic
network by colouring or increasing the width of reaction arrows of the SVG file. FluxVisualizer does
not aim to draw metabolic networks but to use a customer’s SVG file allowing him to exploit his
representation standards with a minimum of constraints. FluxVisualizer is especially suitable for
small to medium size metabolic networks, where a visual representation of the fluxes makes sense.
The flux distribution can either be an elementary flux mode (EFM), a flux balance analysis (FBA)
result or any other flux distribution. It allows the automatic visualization of a series of pathways of
the same network as is needed for a set of EFMs. The software is coded in python3 and provides a
graphical user interface (GUI) and an application programming interface (API). All functionalities
of the program can be used from the API and the GUI and allows advanced users to add their own
functionalities. The software is able to work with various formats of flux distributions (Metatool,
CellNetAnalyzer, COPASI and FAME export files) as well as with Excel files. This simple software
can save a lot of time when evaluating fluxes simulations on a metabolic network.
Keywords: metabolic network visualization; metabolic modelling; elementary flux modes visualization;
flux balance analysis
1. Introduction
The study of genome-scale metabolic models has grown strongly in recent years. This has
stimulated the development of visualization software of large models of metabolism [1–3] for
reviews. At the same time, new methods of studying metabolic fluxes have emerged which lead
to the enumeration of EFMs, Flux Balance Analysis (FBA) [4,5] for reviews alongside more traditional
methods such as dynamical systems and Metabolic Control Analysis [6–8] using the rate functions
of the metabolic steps [9,10]. However only FBA can be applied to the greatest genome-scale
models. As a matter of fact, the number of EFMs highly increases and it is not possible to calculate
them. Furthermore, the rate equations are not entirely known at the level of genome scale model,
particularly the number of enzymes. Consequently, it is not possible to derive a pertinent dynamical
system describing the behaviour of a genome-scale model in a physiological context. For these
reasons, reduced metabolic models, or core models, are often derived to study particular problems
or for a manoeuvrable approach to metabolism [11–13]. In this type of approach drawing a reduced
metabolic network plays an essential role. It usually summarizes the results or hypotheses of
the authors in the form of pathways of different colours or of different sizes according to the
flux values. Many software already exist for automatically generating flux maps for a metabolic
Processes 2018, 6, 39; doi:10.3390/pr6050039
www.mdpi.com/journal/processes
144