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Functional genomic hypothesis generation and experimentation by a robot scientist
Open accessAbstract
The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data are being generated much faster than they can be effectively analysed. We describe a physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises experiments to test these hypotheses, physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses inconsistent with the data, and then repeats the cycle. Here we apply the system to the determination of gene function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth experiments. We built and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid synthesis pathway. In biological experiments that automatically reconstruct parts of this model, we show that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold (respectively), both cheapest and random-experiment selection.
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Raedt, L. D., Blockeel, H., Kolb, S., Teso, S., & Verbruggen, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01768-2_1 · Cited by 5
Metadata
- Authors
- King, R. D., Whelan, K. E., Jones, F. M., Reiser, P. G. K., Bryant, C. H., Muggleton, S. H., Kell, D. B., & Oliver, S. G.
- Affiliations
- Aberystwyth University (GB)
- Robert Gordon University (GB)
- Imperial College London (GB)
- University of Manchester (GB)
- Corresponding author
- Oliver, Stephen G.
- Venue
- Nature
- Published
- 2004-01
- Volume
- 427
- Issue
- 6971
- Pages
- 247-252
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2136636278
- Cited by
- 662
- References
- 29
- Citations by year
- 2026: 26 · 2025: 31 · 2024: 26 · 2023: 54 · 2022: 52 · 2021: 27 · 2020: 30 · 2019: 33 · 2018: 15 · 2017: 25 · 2016: 14 · 2015: 22 · 2014: 25 · 2013: 24 · 2012: 24
- FWCI
- 106.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- University of Salford Institutional Repository (University of Salford) (submittedVersion, cc-by)
- Funders
- National Institute for Health and Care Research (NIHR202203)
- Department of Health and Social Care
- Engineering and Physical Sciences Research Council (GR/R60621/01, GR/R54071/01)
- Biotechnology and Biological Sciences Research Council (E19354)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- robot scientists, automated hypothesis generation, automated experimentation, laboratory automation, gene function analysis, deletion mutants, Saccharomyces cerevisiae, aromatic amino acid biosynthesis, metabolic pathway modeling, artificial intelligence in science
- MeSH terms
- Workforce, Algorithms, Amino Acids, Computer Simulation, Cost-Benefit Analysis, Efficiency, Genes, Fungal, Humans, Learning, Models, Biological, Phenotype, Probability, Research, Research Design, Research Personnel, Robotics, Saccharomyces cerevisiae, Software, Time Factors, Open Reading Frames, Gene Deletion, Computational Biology, Genomics, Saccharomyces cerevisiae Proteins
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