ArticleACS synthetic biology2022
Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site.
Article in ACS synthetic biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Microbial Production of Alternative Proteins for Food Applications: Advances, Industrial Challenges, and Regulatory Status.Comprehensive reviews in food science and food safety · 2026Review
- High-level production of vitamin K2 inSynthetic and systems biotechnology · 2026Article
- Hybrid metabolic engineering enables xylose-driven co-production of polyhydroxybutyrate and violacein in Escherichia coli.Journal of biological engineering · 2026Article
- Tailoring Corynebacterium glutamicum for Sustainable Biomanufacturing: From Traditional to Cutting-Edge Technologies.Molecular biotechnology · 2026Review
- Genetically encoded biosensors enabled high-throughput screening of microbial cell factories.Engineering microbiology · 2026Review
- Activity-Cloud Organization of Shine-Dalgarno Sequences to Guide Translation Engineering inACS omega · 2026Article
- SUPER: Upcycling Genetic Parts for Precise Gene Expression Control, Leakage Minimization, and Genetic Circuit Stability.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A unified computational framework for quantitative design and optimization of transcriptional regulation across bacterial species.Nucleic acids research · 2026Article
- Deep learning on protein language model embeddings unlocks accurate prediction of protein solubility.Frontiers in microbiology · 2026Article
- The knowledge driven DBTL cycle provides mechanistic insights while optimising dopamine production in Escherichia coli.Microbial cell factories · 2025Article
- AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.Molecules (Basel, Switzerland) · 2024Review
- Automated in vivo enzyme engineering accelerates biocatalyst optimization.Nature communications · 2024Review
- Machine Learning for Biological Design.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Article
- Machine learning for metabolic pathway optimization: A review.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimization of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved by engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, the design of specific genetic parts remains challenging because of the lack of reliable design methods. To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to demonstrate how small genetic parts can be reliably designed using relatively small, high-quality data sets. We used Gaussian Process Regression for the Learn phase of the cycle and the Upper Confidence Bound multiarmed bandit algorithm for the Design of genetic variants to be tested in vivo. We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. Notably, by Testing a total of 450 RBS variants in four DBTL cycles, we have experimentally validated RBSs with high translation initiation rates equaling or exceeding our benchmark RBS by up to 34%. Overall, our results show that machine learning is a powerful tool for designing RBSs, and they pave the way toward more complicated genetic devices.
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