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Review
. 2025 Mar;121(5):e70047.
doi: 10.1111/tpj.70047.

Arabidopsis research in 2030: Translating the computable plant

Affiliations
Review

Arabidopsis research in 2030: Translating the computable plant

Siobhan Brady et al. Plant J. 2025 Mar.

Erratum in

  • Correction.
    [No authors listed] [No authors listed] Plant J. 2025 May;122(4):e70221. doi: 10.1111/tpj.70221. Plant J. 2025. PMID: 40388913 Free PMC article. No abstract available.

Abstract

Plants are essential for human survival. Over the past three decades, work with the reference plant Arabidopsis thaliana has significantly advanced plant biology research. One key event was the sequencing of its genome 25 years ago, which fostered many subsequent research technologies and datasets. Arabidopsis has been instrumental in elucidating plant-specific aspects of biology, developing research tools, and translating findings to crop improvement. It not only serves as a model for understanding plant biology and but also biology in other fields, with discoveries in Arabidopsis also having led to applications in human health, including insights into immunity, protein degradation, and circadian rhythms. Arabidopsis research has also fostered the development of tools useful for the wider biological research community, such as optogenetic systems and auxin-based degrons. This 4th Multinational Arabidopsis Steering Committee Roadmap outlines future directions, with emphasis on computational approaches, research support, translation to crops, conference accessibility, coordinated research efforts, climate change mitigation, sustainable production, and fundamental research. Arabidopsis will remain a nexus for discovery, innovation, and application, driving advances in both plant and human biology to the year 2030, and beyond.

Keywords: AI; Arabidopsis thaliana; gene regulatory networks; genomics; model system; translational research.

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Conflict of interest statement

The authors have not declared a conflict of interest.

Figures

Figure 1
Figure 1
A vision for Arabidopsis research in 2030. Computational models created by integrating large ‘omics data using AI would inform experiments in Arabidopsis that could be translated to crop species, especially in the context of climate change. Mechanisms unique to crop species (or coopted by pathogens or parasitic plants) could be elucidated with the help of Arabidopsis. MASC would support accessibility and diversity in conferences and other equity issues, and would help to coordinate research efforts between Arabidopsis and crop researchers/agronomists from around the world. Images on the right from Filament LLC, used with permission.

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