A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning

TL;DR

CLOSEgaps is a deep learning framework that models metabolic networks as hypergraphs to automatically identify and fill missing reactions, significantly improving the accuracy of genome-scale metabolic models without relying on experimental data.

Contribution

It introduces a novel hypergraph-based deep learning approach for automatic gap-filling in GEMs, capable of characterizing both known and hypothetical reactions.

Findings

Achieves over 96% accuracy in filling artificially introduced gaps

Improves phenotypic prediction accuracy across 24 GEMs

Enhances production of key metabolites in two organisms

Abstract

Incomplete knowledge of metabolic processes hinders the accuracy of GEnome-scale Metabolic models (GEMs), which in turn impedes advancements in systems biology and metabolic engineering. Existing gap-filling methods typically rely on phenotypic data to minimize the disparity between computational predictions and experimental results. However, there is still a lack of an automatic and precise gap-filling method for initial state GEMs before experimental data and annotated genomes become available. In this study, we introduce CLOSEgaps, a deep learning-driven tool that addresses the gap-filling issue by modeling it as a hyperedge prediction problem within GEMs. Specifically, CLOSEgaps maps metabolic networks as hypergraphs and learns their hyper-topology features to identify missing reactions and gaps by leveraging hypothetical reactions. This innovative approach allows for the characterization and curation of both known and hypothetical reactions within metabolic networks. Extensive results demonstrate that CLOSEgaps accurately gap-filling over 96% of artificially introduced gaps for various GEMs. Furthermore, CLOSEgaps enhances phenotypic predictions for 24 GEMs and also finds a notable improvement in producing four crucial metabolites (Lactate, Ethanol, Propionate, and Succinate) in two organisms. As a broadly applicable solution for any GEM, CLOSEgaps represents a promising model to automate the gap-filling process and uncover missing connections between reactions and observed metabolic phenotypes.