{
  "abstract": "Genome-scale metabolic models increasingly challenge CPU linear-programming solvers in repeated optimisation. We present gpuGEM, an open-source Python library for GPU-based optimisation using NVIDIA cuOpt, with size-adaptive settings validated against HiGHS and Gurobi. Across ten models spanning four orders of magnitude, gpuGEM achieved 1.1x to 94x speed-ups over the faster HiGHS method (dual simplex or interior point), and outperformed Gurobi on three models (1.6x to 2.5x). In candidate-gene prioritisation, gpuGEM reduced end-to-end query time from 41 s to 21 s. Availability and Implementation: gpuGEM is released under the MIT licence at https://github.com/Digital-Metabolic-Twin-Centre/gpuGEM and on PyPI (pip install gpugem); release v0.1.3 is archived at https://zenodo.org/records/23422813. It requires an NVIDIA GPU of compute capability 7.0 or newer (Volta onward), Python 3.11 or later, CUDA 12 and cuOpt 26.6 or later. Contact: ronan.mt.fleming@gmail.com Supplementary information: Supplementary data are available as an ancillary file on arXiv.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.24106",
  "author_total": 4,
  "authors": [
    {
      "name": "Farid Zare"
    },
    {
      "name": "Timothy Hulshof"
    },
    {
      "name": "Ines Thiele"
    },
    {
      "name": "Ronan M. T. Fleming"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4n5grhxd3h0sg.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.24106",
  "fulltext_url": null,
  "id": "4n5grhxd3h0sg",
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  "landing_url": "https://arxiv.org/abs/2610.24106",
  "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",
      "q-bio.QM"
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      "openalex",
      "1312|Microbial Metabolic Engineering and Bioproduction"
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  ],
  "openalex": "W7223071768",
  "pdf_url": "https://arxiv.org/pdf/2610.24106",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4n5grhxd3h0sg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:36Z",
      "native_id": "2610.24106",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.24106"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "gpuGEM: GPU-based linear optimisation of genome-scale metabolic models",
  "title_text": "gpuGEM: GPU-based linear optimisation of genome-scale metabolic models",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:56Z",
  "url": "https://research.draconitic.com/paper/4n5grhxd3h0sg",
  "venue": "arXiv",
  "versions": []
}