摘要：SummaryGenome-scale metabolic reconstructions include all known biochemical reactions occurring in a cell. A typical application is the prediction of potential drug targets for cancer treatment. The precision of these predictions relies on the definition of the objective function. Generally, the biomass reaction is used to illustrate the growth capacity of a cancer cell. Today, seven human biomass reactions can be identified in published metabolic models. The impact of these differences on the metabolic model predictions has not been explored in detail. We explored this impact on cancer metabolic model predictions and showed that the metabolite composition and the associated coefficients had a large impact on the growth rate prediction accuracy, whereas gene essentiality predictions were mainly affected by the metabolite composition. Our results demonstrate the importance of defining a consensus biomass reaction compatible with most human models, which would contribute to ensuring the reproducibility and consistency of the results.Graphical abstractDisplay OmittedHighlights•The definition of the biomass reaction is of utmost importance for model predictions•Growth rate predictions are affected by metabolite composition and their coefficients•Gene essentiality predictions are mainly affected by the metabolite composition•Need to find a standard biomass reaction for reproducibility and consistency purposesMathematical biosciences; In silico biology; Metabolic flux analysis
