FCDECOMP: Decomposition of metabolic networks based on flux coupling relations

Abstract

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501

“…Such networks enable us to better understand the quantitative dependencies between metabolic elements which may not be recognizable if a metabolic subnetwork is studied in isolation [79][80][81] . Such networks enable us to better understand the quantitative dependencies between metabolic elements which may not be recognizable if a metabolic subnetwork is studied in isolation [79][80][81] .…”

“…Such networks enable us to better understand the quantitative dependencies between metabolic elements which may not be recognizable if a metabolic subnetwork is studied in isolation. [79][80][81] Consequently, these models can serve as tools for intelligent experimental designs like metabolic engineering and drug targeting. 82,83 Furthermore, since these networks represent metabolism in a genome scale, they can be used to predict cellular responses to environmental or genetic perturbations such as nutrient deficiency or mutation in one gene or a set of genes.…”