Extended Data Fig. 2: CLIM workflow integrates multiobjective metabolic flux analysis and machine learning to identify and validate collateral lethal metabolic targets.

(a) Definition of Super Pareto, Infeasible Pareto and Blocked Pareto in context of metabolic deletions. (b) Schematic describing the reconstruction of genome-scale metabolic networks for cancers based on metabolic gene expressions and empirically observed extracellular fluxes. Recon 2.2 is used as the input model with all gene-protein-reaction (GPR) relationships. Average expression of cancer cell-lines and tumor samples along with extracellular fluxes are used to reconstruct a contextual model using the iMAT algorithm. Further, automated and manual curation is performed to edit reactions to fix gaps in the model. Final reconstruction is performed by iMAT’s extension rxnMILP algorithm. (c) Derivation of Pareto area under the curve (PAUC) to quantify overall metabolic changes across the entire Pareto frontier between deleted- and non-deleted conditions. (d) Derivation of therapeutic window index (TWI) using sensitivity analysis to quantify influence of metabolic fluxes on metabolic objective functions. (e) Decision-making heuristic for selection of collateral lethal candidate targets by analyzing PAUC and TWI scores.