Flux sampling and graph neural networks for improved gene essentiality prediction in mammalian genome-scale metabolic models.

Gene essentiality, the requirement of a gene for survival or proliferation, is central to understanding cellular processes and identifying drug targets. Experimental determination requires large growth screens that are time-consuming and expensive, motivating in silico approaches. Existing methods predominantly use flux balance analysis (FBA), a constraint-based optimisation framework that requires a predefined cellular objective function. This can introduce observer bias, because the objective often reflects the researcher's assumptions rather than the cell's biological goals. Here, we present FluxGAT, a graph neural network (GNN) that predicts gene essentiality from graphical representations of flux sampling data. Flux sampling removes the need for an explicit objective and instead characterises feasible steady-state fluxes. FluxGAT combines this information with metabolic network topology to learn flux-informed node representations and classify reactions as essential or non-essential. We apply FluxGAT to the iCHO2291 genome-scale model of Chinese hamster ovary cells and Mouse1, a generic mouse model with independent essentiality labels. In both systems, FluxGAT improves sensitivity over FBA while maintaining high precision and specificity, and recovers more experimentally essential genes, especially where FBA predicts very few essentials. These results show that flux-informed GNNs can provide more general gene essentiality predictions across mammalian genome-scale models without hand-crafted objective functions.