ArticleNature communications2020
A machine learning Automated Recommendation Tool for synthetic biology.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers.
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Who cites it
117 citing papers in PubMed.
- Critical review of artificial intelligence in synthetic biology: DBTL cycle applications, challenges, and design rules for microbial strain engineering.World journal of microbiology & biotechnology · 2026Review
- Application and progress of synthetic biology technology in amino acid production.Bioprocess and biosystems engineering · 2026Review
- Recent advances in AI-driven deep eutectic solvent design for biocatalysis: Co-design of extremozyme and reaction medium.Biodesign research · 2026Review
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- Integrating Machine Learning and Transcriptomics to Enhance β‑Carotene Production inACS omega · 2026Article
- Machine-Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: A p-Coumaric Acid Case Study.ACS synthetic biology · 2026Article
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- Nanofluidics for Pioneering Synthetic Biology of Bottom-Up Cell-Free Molecular Systems.ACS synthetic biology · 2026Review
- A unifying equation for fermentation sustainability across the titer-rate-yield landscape.Nature communications · 2026Article
- Microbial Enzyme Production: Critical Bottlenecks and Integrated Engineering Solutions.Journal of basic microbiology · 2026Review
- Review
- Parabiotics as Next-Generation Microbiome Therapeutics: Insights into Mechanisms, Evidence, and Therapeutic Potential.Current microbiology · 2026Review
- Deep Learning-Based Protein Half-Life Prediction for Identifying Rate-Limiting Enzymes in Metabolic Pathways to Alleviate Bottleneck Reactions.Journal of microbiology and biotechnology · 2026Article
- Rhodo-Box: A Synthetic Biology Toolbox to Facilitate Metabolic Engineering ofACS synthetic biology · 2026Article
- Are we ready for causal discovery in biological systems using deep learning?Briefings in bioinformatics · 2026Article
- Tailoring Corynebacterium glutamicum for Sustainable Biomanufacturing: From Traditional to Cutting-Edge Technologies.Molecular biotechnology · 2026Review
- Yeast as a Model for Human Disease.International journal of molecular sciences · 2026Review
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
57 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.
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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.