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 as probabilities, or 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 study the derived graphs under various growth conditions . 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  condition. By integrating constraint-based models and tools from network science, our framework allows for the interrogation of environment-specific metabolic responses beyond fixed, standard pathway descriptions.
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.
R
as probabilities, or
probabilistically, or can be
R
and study the derived
, and on a larger metabolic model of human hepatocytes, and study the proposed
R
growth conditions
environmental conditions and genetic perturbations
A
and genetic
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environment-specific
context-specific