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.