Cells adapt their metabolism to survive changes in their environment. We present a  framework for the construction and analysis of metabolic reaction networksthat can be tailored to reflect different environmental conditions . Using context-dependent flux distributions from Flux Balance Analysis (FBA), we produce directed networks with weighted links representing the amount of metabolite flowing from a source reaction to a target reaction per unit time. Such networks are analyzed with tools from network theory to reveal salient features of metabolite flows in each biological context. We illustrate our approach with the directed network of the central carbon metabolism of Escherichia coli , and study its properties in four relevant biological scenarios. Our results show that both flow and network structure depend drastically on the environment: networks produced from the same metabolic model in different contexts have different edges, components, and flow communities, capturing the biological re-routing of metabolic flows inside the cell . By integrating FBA-based analysis with tools from network science, our results provide a framework to interrogate cellular metabolism beyond standard pathway descriptions that are blind to the environmental context .
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 .
R
metabolism to survive changes in their
metabolic fluxes in response to changes in the
A
systematic flux-based
R
and analysis of metabolic reaction networksthat can be tailored to reflect
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
R
. Using context-dependent flux distributions from
by incorporating flux distributions computed from constraint-based modelling such as
R
(FBA), we produce directed networks with weighted links representing the amount of metabolite flowing from a source reaction to a target reaction per unit time. Such networks are analyzed with tools from network theory to reveal salient features of metabolite flows in each biological context.
.
R
with the directed network of the
on the
R
, and study its properties in four relevant biological scenarios. Our results show that both flow and network structure depend drastically on the environment: networks produced from the same metabolic model in different contexts have different edges, components, and flow communities, capturing the biological
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
R
flows inside the cell
fluxes under each growth condition
R
FBA-based analysis with
constraint-based models and
R
results provide a framework to interrogate cellular metabolism beyond
framework allows for the interrogation of environment-specific metabolic responses beyond fixed,
D
that are blind to the environmental context