ReviewFEMS yeast research2026
High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.
Review in FEMS yeast research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
High-throughput yeast engineering is being transformed by biofoundries that integrate automation, artificial intelligence (AI), and standardized workflows. This review examines how these facilities accelerate strain development through the Design-Build-Test-Learn (DBTL) cycle, with advances in genome editing, phenotypic screening, and predictive modelling. It highlights Australia's involvement through the Australian Genome Foundry, Idea-BIO, and the CSIRO Biofoundiry and explores global efforts to overcome reproducibility and standardization challenges. Despite progress, key barriers remain, including protocol variability and integration of AI tools. We also highlight the opportunity for a shift toward autonomous, self-optimizing 'self-driving labs' that transition from DBTL to Design-Build-Deploy cycles. The future of yeast engineering depends not only on technological innovation, but also on the harmonization of international standards, data governance, and ethical safeguards. If fully realized, the convergence of robotics, AI, and synthetic biology will redefine yeast engineering, leading to step changes in strain performance for a variety of important products, thus enabling economic and sustainable biomanufacturing at scale.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.