  Gene essentiality, the necessity of a specific gene for the survival of an
organism, is crucial to our understanding of cellular processes and identifying
drug targets. Experimental determination of gene essentiality requires large
growth screens that are time-consuming and expensive, motivating the
development of in-silico approaches. Existing methods predominantly utilise
flux balance analysis (FBA), a constraint-based optimisation algorithm;
however, they are fundamentally limited by the necessity of a predefined
cellular objective function. This requirement introduces an element of observer
bias, as the objective function often reflects the researcher's assumptions
rather than the cell's biological goals. Here, we present FluxGAT, a graph
neural network (GNN) model capable of predicting gene essentiality directly
from graphical representations of flux sampling data. Flux sampling removes the
need for objective functions, thereby eliminating observer bias. FluxGAT
leverages the unique strengths of GNNs in learning representations of complex
relationships within metabolic reaction networks. The success of our approach
in predicting experimentally determined gene essentiality, with almost double
the sensitivity of FBA, explores the possibility of predicting cellular
phenotypes in cases when objectives are less understood. Thus, we demonstrate a
method for more general gene essentiality predictions across a broader spectrum
of biological systems and environments.
