A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning
Journal:
arXiv
Published Date:
Sep 20, 2024
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.
