ReviewComputational and structural biotechnology journal2023
Machine learning for metabolic pathway optimization: A review.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- Review
- High-throughput editing boosts the construction of next-generation microbial cell factories.Synthetic and systems biotechnology · 2026Review
- Knowledge-driven interpretable neural networks provide mechanistic insight.Genome research · 2026Article
- Tailoring Corynebacterium glutamicum for Sustainable Biomanufacturing: From Traditional to Cutting-Edge Technologies.Molecular biotechnology · 2026Review
- Precision hydrolysis: tailored yeast processing enzymes for yeast-based products.Applied microbiology and biotechnology · 2026Review
- Review
- Biological parts in yeast synthetic biology: from regulatory elements to predictive design platforms.FEMS yeast research · 2026Review
- Advances in mitochondrial compartmentalization for terpenoid synthesis inFrontiers in bioengineering and biotechnology · 2026Review
- Engineering of Synthetic Microbial Consortia for Sustainable Management of Wastewater and Polyethylene Terephthalate: A Comprehensive Review.International journal of molecular sciences · 2025Review
- Exploring pigment-producing Streptomyces as an alternative source to synthetic pigments: diversity, biosynthesis, and biotechnological applications. A review.World journal of microbiology & biotechnology · 2025Review
- Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers.Biomolecules & biomedicine · 2025Review
- Engineering Useful Microbial Species for Pharmaceutical Applications.Microorganisms · 2025Review
- Hierarchical metabolic engineering for rewiring cellular metabolism.FEMS microbiology reviews · 2025Review
- Unlocking the potential of flavonoid biosynthesis through integrated metabolic engineering.Frontiers in plant science · 2025Review
- Metabolic Fluxes Using Deep Learning Based on Enzyme Variations:International journal of molecular sciences · 2024Article
- Tunable translation-level CRISPR interference by dCas13 and engineered gRNA in bacteria.Nature communications · 2024Article
- Automated in vivo enzyme engineering accelerates biocatalyst optimization.Nature communications · 2024Review
- Promising non-model microbial cell factories obtained by genome reduction.Frontiers in bioengineering and biotechnology · 2024Review
- Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.International journal of molecular sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimizing the metabolic pathways of microbial cell factories is essential for establishing viable biotechnological production processes. However, due to the limited understanding of the complex setup of cellular machinery, building efficient microbial cell factories remains tedious and time-consuming. Machine learning (ML), a powerful tool capable of identifying patterns within large datasets, has been used to analyze biological datasets generated using various high-throughput technologies to build data-driven models for complex bioprocesses. In addition, ML can also be integrated with Design-Build-Test-Learn to accelerate development. This review focuses on recent ML applications in genome-scale metabolic model construction, multistep pathway optimization, rate-limiting enzyme engineering, and gene regulatory element designing. In addition, we have discussed some limitations of these methods as well as potential solutions.
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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.