FluxGAT: Integrating Flux Sampling with Graph Neural Networks for Unbiased Gene Essentiality Classification

TL;DR

FluxGAT is a novel graph neural network model that predicts gene essentiality from flux sampling data, eliminating observer bias inherent in traditional methods and achieving higher sensitivity in predictions.

Contribution

This paper introduces FluxGAT, a GNN-based approach that uses flux sampling data to predict gene essentiality without relying on predefined cellular objectives.

Findings

FluxGAT nearly doubles the sensitivity of FBA in predicting gene essentiality.

Flux sampling removes the need for an objective function, reducing observer bias.

FluxGAT enables more general gene essentiality predictions across diverse biological systems.

Abstract

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.