F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

Abdelhalim Larhlimi,Joachim Selbig,Alexander Bockmayr,Laszlo David

doi:10.1186/1471-2105-13-57

Abstract

BackgroundFlux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions.ResultsWe present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner.ConclusionsWe propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.

Highlights

Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks
We focus on flux coupling methods based on solving a sequence of linear programming (LP) problems
In analogy with the WRP-Flux Coupling Finder (FCF) and Feasibility-based Flux Coupling Analysis (FFCA) approaches, we identify the reversibility type of reactions in order to apply linear programming only in those cases where coupling relationships can occur [33]

Summary

Introduction

Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. Constraint-based analysis is based on the application of a series of constraints that govern the operation of a metabolic network at steady state. This includes the stoichiometric and thermodynamic constraints, which limit the range of possible behaviors of the metabolic network, corresponding to different metabolic phenotypes. Applying these constraints leads to the definition of the solution space, called the steady-state flux cone [11]:.

Methods

Results

Conclusion