ReviewMetabolic engineering communications2020
Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering.
Review in Metabolic engineering communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 51 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
51 citing papers in PubMed, 120 citations in OpenAlex.
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- Metabolic engineering strategies: utilizing different microbial strains and advanced technologies for the synthesis of high-valued terpenoids.Antonie van Leeuwenhoek · 2026Review
- Advances in Functional Genomics and Biotechnology for Enhancing Therapeutic Potential of Medicinal Plants.International journal of molecular sciences · 2026Review
- Sustainable bioenergy manufacturing in plants.Plant communications · 2026Review
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- Systems biology in the era of AI: "winter" or "evolution"?Frontiers in systems biology · 2026Article
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Applications of Metabolomics in the Development of Biomedicine and Biotechnology.Advances in experimental medicine and biology · 2026Review
- Network biology to artificial intelligence: building the next generation of predictive drug discovery.Frontiers in immunology · 2026Review
- Kinetic modeling of terpenoid production in E. coli: insights into subpopulation emergence and process optimization.Microbial cell factories · 2025Article
- Development and application of synthetic biology tools for improved production of human breast milk components.Synthetic and systems biotechnology · 2025Review
- Artificial Intelligence in Edible Mushroom Cultivation, Breeding, and Classification: A Comprehensive Review.Journal of fungi (Basel, Switzerland) · 2025Review
- Artificial intelligence in biology and medicine.Die Naturwissenschaften · 2025Review
- Microbial dysbiosis and associated disease mechanisms in maternal and child health.Infection and immunity · 2025Review
- Combining biotechnology with sustainability: feasibility of bioenergy sorghum in generating high-value bioproducts.Biotechnology letters · 2025Review
- Engineering biology applications for environmental solutions: potential and challenges.Nature communications · 2025Review
- BeyondChemical reviews · 2025Review
- The single-cell revolution in transplantation: high-resolution mapping of graft rejection, tolerance, and injury.Frontiers in immunology · 2025Review
- Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli.NPJ systems biology and applications · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.
Indexed as
Identifiers
What Socratic 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 Socratic graph.