GraphGDel: Constructing and Learning Graph Representations of Genome-Scale Metabolic Models for Growth-Coupled Gene Deletion Prediction

[2504.06316] GraphGDel: Constructing and Learning Graph Representations of Genome-Scale Metabolic Models for Growth-Coupled Gene Deletion Prediction Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Quantitative Methods arXiv:2504.06316 (q-bio) [Submitted on 8 Apr 2025 ( v1 ), last revised 9 Apr 2026 (this version, v7)] Title: GraphGDel: Constructing and Learning Graph Representations of Genome-Scale Metabolic Models for Growth-Coupled Gene Deletion Prediction Authors: Ziwei Yang , Takeyuki Tamura View a PDF of the paper titled GraphGDel: Constructing and Learning Graph Representations of Genome-Scale Metabolic Models for Growth-Coupled Gene Deletion Prediction, by Ziwei Yang and 1 other authors View PDF HTML (experimental) Abstract: In genome-scale constraint-based metabolic models, gene deletion strategies are essential for achieving growth-coupled production, where cell growth and target metabolite synthesis occur simultaneously. Despite the inherently networked nature of genome-scale metabolic models, existing computational approaches rely primarily on sequential data and lack graph representations that capture their complex relationships, as both well-defined graph constructions and learning frameworks capable of exploiting them remain largely unexplored. To address this gap, we present a twofold solution. First, we introduce a systematic pipeline for constructing graph representations from constraint-based metabolic models. Second, we develop a deep learning framework that integrates these graph representations with gene and metabolite sequence data to predict growth-coupled gene deletion strategies. Across three metabolic models, our approach consistently outperforms established baselines, with improvements in overall accuracy of 14.04%, 16.26%, and 13.18% over a deep feedforward neural network baseline, 6.17%, 4.96%, and 5.31% over a sequence-learning baseline, and 5.10%, 4.36%, and 4.70% over a topology-aware graph aggregation baseline on the same metabolite graph, respectively. The source code and example datasets are available at: this https URL . Subjects: Quantitative Methods (q-bio.QM) ; Machine Learning (cs.LG) Cite as: arXiv:2504.06316 [q-bio.QM] (or arXiv:2504.06316v7 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2504.06316 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ziwei Yang [ view email ] [v1] Tue, 8 Apr 2025 08:07:59 UTC (298 KB) [v2] Thu, 12 Jun 2025 07:02:42 UTC (301 KB) [v3] Fri, 13 Jun 2025 03:21:23 UTC (301 KB) [v4] Thu, 19 Jun 2025 05:07:51 UTC (301 KB) [v5] Fri, 3 Oct 2025 07:01:28 UTC (301 KB) [v6] Mon, 10 Nov 2025 04:24:28 UTC (383 KB) [v7] Thu, 9 Apr 2026 09:00:10 UTC (381 KB) Full-text links: Access Paper: View a PDF of the paper titled GraphGDel: Constructing and Learning Graph Representations of Genome-Scale Metabolic Models for Growth-Coupled Gene Deletion Prediction, by Ziwei Yang and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.QM < prev | next > new | recent | 2025-04 Change to browse by: cs cs.LG q-bio References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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