{
  "abstract": "Designing microbial strains for growth-coupled production, in which cellular growth is linked to the synthesis of a target compound through genetic interventions, is a fundamental goal in metabolic engineering. Constraint-based methods can identify growth-coupled deletion strategies, but design for each target typically requires a separate search across a large number of possible gene-deletion combinations. This motivates a reusable approach that learns from previously evaluated strain designs to prioritize candidate interventions before downstream constraint-based evaluation. We present a reproducible protocol for growth-coupled gene deletion design that learns and ranks gene-deletion priorities for each target metabolite. Using computationally curated deletion strategies as learning resources, the protocol generates continuous deletion-priority rankings, constructs candidate strategies at adjustable search-space budgets, and evaluates these candidates through constraint-based metabolic analysis. Its learning component, HGPRGDel, explicitly represents gene-protein-reaction (GPR) logic, capturing how multiple genes can jointly support a reaction or alternative genes can maintain its activity, while integrating molecular and reaction-level information. Across e_coli_core, iMM904, and iML1515, the protocol achieves the highest macro-F1 and AUC among the evaluated learning configurations. HGPRGDel's rankings recovered an average of 90% of the reference deletion genes from MetNetComp by examining fewer genes than the evaluated baseline methods. By separating learned intervention prioritization from downstream constraint-based assessment, the protocol provides a reproducible framework for reusing strain-design knowledge while controlling the size of the candidate space and retaining mechanistic evaluation of growth-coupled production.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.37478",
  "author_total": 2,
  "authors": [
    {
      "name": "Ziwei Yang"
    },
    {
      "name": "Takeyuki Tamura"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1hjwytnknadqt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.37478",
  "fulltext_url": null,
  "id": "1hjwytnknadqt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.37478",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1312|Microbial Metabolic Engineering and Bioproduction"
    ]
  ],
  "openalex": "W7224101029",
  "pdf_url": "https://arxiv.org/pdf/2610.37478",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1hjwytnknadqt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:56Z",
      "native_id": "2610.37478",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.37478"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "HGPRGDel: GPR-Aware Learning and Ranking for Growth-Coupled Gene Deletion Design",
  "title_text": "HGPRGDel: GPR-Aware Learning and Ranking for Growth-Coupled Gene Deletion Design",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:56Z",
  "url": "https://research.draconitic.com/paper/1hjwytnknadqt",
  "venue": "arXiv",
  "versions": []
}