Flux-dependent graphs for metabolic networks

TL;DR

This paper introduces a novel framework for constructing flux-based graphs from metabolic networks, capturing systemic changes in fluxes under different conditions using network science tools.

Contribution

The authors develop a systematic method to create flux-dependent graphs that encode metabolic flux directionality and can incorporate probabilistic or condition-specific flux data.

Findings

Flux-dependent graphs reveal systemic topological changes under environmental shifts.

The approach captures re-routing of metabolic fluxes and reaction importance.

Framework integrates constraint-based models with network analysis for system-level insights.

Abstract

Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topological and community structure, which capture the re-routing of metabolic fluxes and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions.