Flux-dependent graphs for metabolic networks

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.