gpuGEM: GPU-based linear optimisation of genome-scale metabolic models

Genome-scale metabolic models increasingly challenge CPU linear-programming solvers in repeated-optimisation workflows.

By Zare†, Hulshof†, Thiele +1Oct 16, 2026

Score████░░░░░░4.3

Key numbers

1.1x to 94
1.6x to 2.5

Caveats

Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

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Abstract

Summary: Genome-scale metabolic models increasingly challenge CPU linear-programming solvers in repeated-optimisation workflows. We present gpuGEM, an open-source Python library for GPU-based GEM optimisation using NVIDIA cuOpt, with size-adaptive settings validated against HiGHS and Gurobi. Across a ten-model benchmark suite spanning four orders of magnitude in problem size, gpuGEM achieved speed-ups of 1.1x to 94x relative to the faster of the HiGHS dual-simplex and interior-point methods for each model. gpuGEM also outperformed Gurobi on three of ten models, with speed-ups of 1.6x to 2.5x. In a candidate-gene prioritisation workflow, 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). An archived release(v0.1.2) is available at https://zenodo.org/records/23422813. gpuGEM requires an NVIDIA GPU, Python 3.11 or later, CUDA 12 and cuOpt 26.6 or later. cuOpt requires a GPU with Volta architecture or newer (compute capability 7.0 or newer). Contact: ronan.mt.fleming@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Farid Zare†, Timothy Hulshof†, Ines Thiele, Ronan M. T. Fleming

The editor's rubric

Heuristic review

Leverage3 / 5: A method or resource many groups across the field will adopt within a year.; 24% of this desk’s rubric
Magnitude4 / 5: A qualitative jump: a capability or regime that did not exist before.; 16% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 20% of this desk’s rubric
Novelty2 / 5: A new combination of known ideas.; 20% of this desk’s rubric
Trajectory3 / 5: A clear path to scale.; 10% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	████░ 4	16%	A qualitative jump: a capability or regime that did not exist before.
Evidence	███░░ 3	20%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	20%	A new combination of known ideas.
Trajectory	███░░ 3	10%	A clear path to scale.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); gains (x-fold, orders of magnitude, outperforms); verification (multiple benchmarks, experimental validation, code released); scale (scalable, orders of magnitude, efficient).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.3

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.7 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.8 / 10; Shrunk toward the desk prior by editor confidence (48%).
Attention: 0%; Citations, upvotes, points, mentions.
Freshness: 71%; Half-life decay since publication.

No attention signals recorded yet.

The record

Reviewed by heuristic-v7 on Oct 20, 2026, 05:48 UTC. Paper type: method.
Categories: q-bio.QM
BRIEF, No.1 in the Biology edition of October 20, 2026.