Figure 1. (A) Schematic representation of the
integrated host-virus metabolic modelling approach used in this article.
The biomass composition of SARS-CoV-2 is estimated as described inMethods and then embedded in the metabolic network model of the
host cell. This model is then used to predict the metabolic fluxes
supporting virus production and effects of perturbations as described in
the main text. (B) Composition of virus biomass in mmol per
gram of virus biomass dry weight. The two panels show amino acids and
nucleotides as labelled. (C) Graph representation of part of
the human cellular metabolic network, focusing on those reactions that
are active in virus production under minimal media conditions with
uptake fluxes set to -10
mmol∙gDW-1∙h-1 (see main text andMethods ). The cell is shown as a circle with a grey background,
with the mitochondrial matrix shown in white background. Nodes are
metabolites and edges are reactions. Edges between two nodes are drawn
when at least one reaction connecting those two metabolites carries flux
in the optimal flux distribution. The graph shown here is obtained from
the full metabolic network by selecting those edges corresponding to the
shortest path between metabolites present in the medium and the
precursor metabolites to amino acids that contribute to the virus
biomass. The paths are found by using the Dijkstra algorithm (Dijkstra
1959) and weighting the edges by the inverse of their flux value. Nodes
are coloured by their cellular location; cytosol (black), mitochondrial
matrix (grey), external medium (green). Node labels are coloured also
according to primarily location, with amino acids and media components
labelled in green and yellow respectively. Key reactions discussed in
the text and summary results are labelled on their corresponding edges.