ReviewTrends in biotechnology2026
Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.
Review in Trends in biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- AI-enabled fungal biomanufacturing: from genome mining to the artificial intelligence virtual cell.Mycology · 2026Review
- Molecular adaptations and engineering of extremophiles for synthetic biology and biotechnological applications.Frontiers in microbiology · 2026Review
- A compact model of Escherichia coli core and biosynthetic metabolism.PLoS computational biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Genome-scale metabolic models are used in fields ranging from metabolic engineering to drug discovery and microbiome design. Although these models are often used to predict putatively optimal states, some applications, including modeling human tissues for drug development and microbial communities for synthetic ecology, may require sampling the whole space of feasible fluxes to obtain distributions of biologically relevant states. Additionally, many applications involve using transcriptomic or proteomic data to predict fluxes for specific tissues, diseases, or patients. We revisit different methods used toward these goals and focus on their limitations and challenges, providing guidelines on how to avoid some of the shortcomings of existing approaches and highlighting conceptual barriers that will require new methodologies and offer opportunities for future development.
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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.