Accurate prediction of gene deletion phenotypes with Flux Cone Learning

Abstract

Predicting the impact of gene deletions is crucial for biological discovery, biomedicine, and biotechnology. For example, identifying lethal deletions is key for new cancer therapies or antimicrobial treatments that bypass drug resistance. In biotechnology, non-lethal deletions are a powerful strategy to redirect chemical flux toward production of high-value compounds for the food, energy, and pharmaceutical sectors, using genetically engineered cells as an alternative to petrochemicals. Owing to the cost and complexity of large-scale deletion screens, there is a growing interest in computational models that can leverage such data for predictive modelling. Here, we present Flux Cone Learning, a general framework for predicting the impact of metabolic gene deletions on many phenotypes of interest. Flux Cone Learning is based on high-dimensional Monte Carlo sampling of the metabolic space in tandem with supervised learning of fitness scores from deletion screens. This strategy enables the method to learn correlations between the geometry of the metabolic space and a target deletion phenotype. We demonstrate best-in-class predictive accuracy for metabolic gene essentiality in organisms of varied complexity (Escherichia coli, Saccharomyces cerevisiae, Chinese Hamster Ovary cells), outperforming the gold standard predictions of Flux Balance Analysis. The method does not rely on an optimality principle and thus can be applied to a range of organisms where cellular objectives cannot be encoded as an optimization task. We showcase the versatility of Flux Cone Learning in other phenotypes by training a predictor of small molecule production from deletion screening data. Flux Cone Learning provides a widely applicable framework for phenotypic prediction and lays the groundwork for the development of metabolic foundation models across the kingdom of life.