ReviewMolecular biotechnology2026
Tailoring Corynebacterium glutamicum for Sustainable Biomanufacturing: From Traditional to Cutting-Edge Technologies.
Review in Molecular biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the workhorse of industrial amino acid production, Corynebacterium glutamicum is the focus of this review, which provides a comprehensive overview of available techniques employed to engineer strains with desired traits. The review highlights both traditional and cutting-edge approaches with a brief introduction to the bacterium's physiology, serving as a foundation for understanding its metabolic capabilities and potential applications. Genome modulation techniques by contrasting traditional methods with CRISPR-based approaches, as well as transcription modulation strategies that enhance gene expression and metabolic flux, and high-throughput techniques that streamline strain development processes are summarized. Furthermore, the roles of artificial intelligence and machine learning in genetic engineering are explored, emphasizing their growing impact on strain development.
Indexed as
Identifiers
40493161What 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.