ArticleNature communications2019
Towards a fully automated algorithm driven platform for biosystems design.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
What it found
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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.
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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.
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Who cites it
72 citing papers in PubMed.
- Engineered bacteria in disease diagnosis and therapy: A synthetic biology perspective.Synthetic and systems biotechnology · 2026Review
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- Machine-Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: A p-Coumaric Acid Case Study.ACS synthetic biology · 2026Article
- A unifying equation for fermentation sustainability across the titer-rate-yield landscape.Nature communications · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Exploring chromosomal position effects for predictable tuning of metabolic pathways in yeast.Nucleic acids research · 2026Article
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Biological parts in yeast synthetic biology: from regulatory elements to predictive design platforms.FEMS yeast research · 2026Review
- Comparing metabolic engineering scenarios using simulated design-build-test-learn-cycles.Frontiers in bioengineering and biotechnology · 2026Article
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Multiobjective Design of Growth Media with Genome-Scale Metabolic Models and Bayesian Optimization.Computational and structural biotechnology journal · 2026Article
- Automated Genetic Manipulation for the Construction ofChem & bio engineering · 2025Article
- Optimized Machine Learning for Autonomous Enzymatic Reaction Intensification in a Self-Driving Lab.Biotechnology and bioengineering · 2025Article
- FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets.Bioinformatics (Oxford, England) · 2025Article
- A generalized platform for artificial intelligence-powered autonomous enzyme engineering.Nature communications · 2025Article
- The convergence of AI and synthetic biology: the looming deluge.npj biomedical innovations · 2025Article
- Autonomous 'self-driving' laboratories: a review of technology and policy implications.Royal Society open science · 2025Review
- Synthetic Biology-Based Engineering Cells for Drug Delivery.Exploration (Beijing, China) · 2025Review
- Integrating protein language models and automatic biofoundry for enhanced protein evolution.Nature communications · 2025Article
- Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays.Biotechnology and bioengineering · 2025Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.
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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.