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

• URL: http://arxiv.org/abs/2504.06316v6
• Date: Mon, 10 Nov 2025 04:24:28 GMT
• Title: GraphGDel: Constructing and Learning Graph Representations of Genome-Scale Metabolic Models for Growth-Coupled Gene Deletion Prediction
• Authors: Ziwei Yang, Takeyuki Tamura,
• Abstract summary: We introduce a systematic pipeline for constructing graph representations from constraint-based metabolic models.<n>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.<n>Our approach consistently outperforms established baselines, achieves improvements of 14.04%, 16.26%, and 13.18% in overall accuracy.
• Score: 1.0909000338858534
• License: http://creativecommons.org/licenses/by/4.0/
• 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 of varying scale, our approach consistently outperforms established baselines, achieves improvements of 14.04%, 16.26%, and 13.18% in overall accuracy. The source code and example datasets are available at: https://github.com/MetNetComp/GraphGDel.

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