Cells adapt their metabolic fluxes in response to changes in the environment. We present a systematic flux-based framework for the construction of graphs to URLanism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via links 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 from constraint-based modelling such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli , and on a larger metabolic model of human hepatocytes , and study the proposed graphs under various environmental conditions and genetic perturbations . The results reveal drastic changes in the topological and community structure of the metabolic graphs , which capture the re-routing of metabolic fluxes under each growth and genetic condition . By integrating constraint-based models and tools from network science, our framework allows for the interrogation of context-specific metabolic responses beyond fixed, standard pathway descriptions.
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 URLanism-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.
D
systematic flux-based
R
construction of graphs to
systematic construction of flux-based graphs derived
R
links
edges
R
from
through
R
modelling
approaches
D
,
D
larger
R
, and study the proposed
. The flux-dependent
R
. The results reveal drastic changes in the
exhibit systemic changes in their
D
of the metabolic graphs
R
under each growth and genetic condition
and the varying importance of specific reactions and pathways
R
for the interrogation
the study
R
beyond fixed,
at a system level beyond