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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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Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Reflecting on reflexive thematic analysis
Braun, V., & Clarke, V. (2019) · Qualitative Research in Sport, Exercise and Health · doi:10.1080/2159676x.2019.1628806 · Cited by 19,677
Sample Size in Qualitative Interview Studies: Guided by Information Power
Malterud, K., Siersma, V. D., & Guassora, A. D. (2016) · Qualitative Health Research · doi:10.1177/1049732315617444 · Cited by 12,703
The Logic of Scientific Discovery
Jason, G. (1988) · Encyclopedia of Research Design · doi:10.4135/9781412961288.n223 · Cited by 9,743
The Logic of Scientific Discovery.
Hutchison, T. W., & Popper, K. R. (1959) · Economica · doi:10.2307/2550489 · Cited by 7,629
Hunter, D. J., & Bierma‐Zeinstra, S. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30417-9 · Cited by 5,127
Functional profiling of the Saccharomyces cerevisiae genome
Giaever, G., Chu, A. M., Ni, L., Connelly, C., Riles, L., Véronneau, S., Dow, S., Lucau-Danila, A., Anderson, K., André, B., et al. (2002) · Nature · doi:10.1038/nature00935 · Cited by 4,316
Brachmann, C. B., Davies, A., Cost, G. J., Caputo, E. M., Li, J., Hieter, P., & Boeke, J. D. (1998) · Yeast · doi:10.1002/(sici)1097-0061(19980130)14:2 · Cited by 3,418
Glyn-Jones, S., Palmer, A. J. R., Agricola, R., Price, A. J., Vincent, T. L., Weinans, H., & Carr, A. J. (2015) · The Lancet · doi:10.1016/s0140-6736(14)60802-3 · Cited by 3,151
Flagged first, then by citations
Research works printed but not linked: 6
- Buchanan, B. G., Sutherland, G. L. & Feigenbaum, E. A. in Machine Intelligence Vol. 4 (eds Meltzer, B. & Michie, D.) 209–254 (Edinburgh Univ. Press, 1969)
- Z̈ytkow, J. M., Zhu, J. & Hussam, A. Automated discovery in a chemistry laboratory. in Proceedings of the 8th National Conference on Artificial Intelligence (AAAI-1990) (eds Dietterich, T. & Swartout, W.) 889–894 (MIT, Cambridge, Massachusetts, 1990)
- Pierce, C. S. Collected Papers of Charles Sanders Pierce (Harvard Univ. Press, 1958)
- Mitchell, T. M. Machine Learning (McGraw-Hill, New York, 1997)
- Muggleton, S. & Page, D. in Machine Intelligence Vol. 15 (eds Furukawa, K., Michie, D. & Muggleton, S.) 248–267 (Oxford Univ. Press, 1999)
- Cochran, W. G. & Cox, G. M. Experimental Designs (Wiley, New York, 1992)
Cited by
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Items 101–200 of 661
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Caschera, F., Bedau, M. A., Buchanan, A., Cawse, J. N., Lucrezia, D. D., Gazzola, G., Hanczyc, M. M., & Packard, N. H. (2011) · Biotechnology and Bioengineering · doi:10.1002/bit.23178 · Cited by 81
Reker, D., Schneider, P., & Schneider, G. (2016) · Chemical Science · doi:10.1039/c5sc04272k · Cited by 81
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Soldatova, L. N., Clare, A., Sparkes, A., & King, R. D. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl207 · Cited by 72
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Yu, H., Jo, K., Kounovsky‐Shafer, K. L., de Pablo, J. J., & Schwartz, D. C. (2009) · Journal of the American Chemical Society · doi:10.1021/ja900372m · Cited by 73
Paget, C. M., Schwartz, J., & Delneri, D. (2014) · Molecular Ecology · doi:10.1111/mec.12930 · Cited by 73
Silberglitt, R., Antón, P. S., Howell, D., Wong, A., Gassman, N. R., Jackson, B. A., Landree, E., Pfleeger, S. L., Newton, E. M., & Wu, F. (2006) · RAND Corporation eBooks · doi:10.7249/tr303 · Cited by 71
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Muggleton, S. (2006) · Nature · doi:10.1038/440409a · Cited by 64
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Knowles, J. D. (2009) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2009.933095 · Cited by 61
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Džeroski, S., Pat Langley, B., & Todorovski, L. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73920-3_1 · Cited by 61
Bird, A. (2022) · doi:10.1093/oso/9780199606658.001.0001 · Cited by 67
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Ma, L., Ding, B., Das, S., & Swaminathan, A. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3389768 · Cited by 59
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Wang, L., Hu, X., Yuan, B., & Lu, J. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.06.042 · Cited by 59
Ament, Z., Masoodi, M., & Griffin, J. L. (2012) · Genome Medicine · doi:10.1186/gm331 · Cited by 60
Kell, D. B. (2012) · BioEssays · doi:10.1002/bies.201100144 · Cited by 60
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Lu, D.-Y., Chen, X. L., & Ding, J. (2005) · Medical Hypotheses · doi:10.1016/j.mehy.2005.07.023 · Cited by 52
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Arora, S., Nyberg, E., & Rosé, C. P. (2009) · doi:10.3115/1564131.1564136 · Cited by 46
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Zhong, J., Riordon, J., Wu, T., Edwards, H. S., Wheeler, A. R., Pardee, K., Aspuru‐Guzik, A., & Sinton, D. A. (2019) · Lab on a Chip · doi:10.1039/c9lc01042d · Cited by 40
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Wang, Z., Zhao, W., Hao, G., & Song, B.-A. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.09.009 · Cited by 39
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Inoue, K., Sato, T., Ishihata, M., Kameya, Y., & Nabeshima, H. (2009) · Cited by 38
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Wilkinson, S. J., Benson, N., & Kell, D. B. (2007) · Molecular BioSystems · doi:10.1039/b707506e · Cited by 37
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Zhang, K., Gong, X., & Jiang, Y. L. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202315177 · Cited by 34
Willsey, M., Stephenson, A. P., Takahashi, C., Vaid, P., Nguyen, B. H., Piszczek, M., Betts, C., Newman, S. S., Joshi, S., Strauß, K., et al. (2019) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3297858.3304027 · Cited by 33
Anahideh, H., Asudeh, A., & Thirumuruganathan, S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116981 · Cited by 56
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Inoue, K., Doncescu, A., & Nabeshima, H. (2013) · Machine Learning · doi:10.1007/s10994-013-5341-z · Cited by 34
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Pat Langley, B., & Arvay, A. (2019) · Synthese Library/Synthese library · doi:10.1007/978-3-030-23769-1_11 · Cited by 32
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Soldatova, L., & Rzhetsky, A. (2011) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-2-s2-s9 · Cited by 32
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Kramer, M. H., Farré, J.-C., Mitra, K., Yu, M. K., Ono, K., Demchak, B., Licon, K., Flagg, M., Balakrishnan, R., Cherry, J. M., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2016.12.024 · Cited by 31
van Otterlo, M. (2014) · Surveillance & Society · doi:10.24908/ss.v12i2.4600 · Cited by 33
Caschera, F., Gazzola, G., Bedau, M. A., Moreno, C. B., Buchanan, A. S., Cawse, J. N., Packard, N. H., & Hanczyc, M. M. (2009) · PLoS ONE · doi:10.1371/journal.pone.0008546 · Cited by 31
Shanbedi, M., Heris, S. Z., Amiri, A., Adyani, S., Alizadeh, M., & Baniadam, M. (2014) · Numerical Heat Transfer Part A Applications · doi:10.1080/10407782.2014.892388 · Cited by 31
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Li, Y., Wang, Y., Yu, D., Ye, N., Hu, P., & Zhao, R. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2897307 · Cited by 33
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Hope, T., Downey, D., Weld, D. S., Etzioni, O., & Horvitz, E. (2023) · Communications of the ACM · doi:10.1145/3576896 · Cited by 42
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Grave, K. D., Ramon, J., & Raedt, L. D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88411-8_19 · Cited by 29
Automating Sciences: Philosophical and Social Dimensions
King, R. D., Schüler‐Costa, V., Mellingwood, C., & Soldatova, L. (2018) · IEEE Technology and Society Magazine · doi:10.1109/mts.2018.2795097 · Cited by 30
Self-citationComputational Scientific Discovery in Psychology
Bartlett, L., Pirrone, A., Javed, N., & Gobet, F. R. (2022) · Perspectives on Psychological Science · doi:10.1177/17456916221091833 · Cited by 37
Phenotype differentiation of three E. coli strains by GC-FID and GC–MS based metabolomics
Tian, J., Chunyun, S., Gao, P., Yuan, K., Yang, D., Lu, X., & Xu, G. (2008) · Journal of Chromatography B · doi:10.1016/j.jchromb.2008.06.031 · Cited by 28
Elucidating regulatory mechanisms downstream of a signaling pathway using informative experiments
Szczurek, E., Gat‐Viks, I., Tiuryn, J., & Vingron, M. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.45 · Cited by 28
Incorporating symbolic domain knowledge into graph neural networks
Dash, T., Srinivasan, A., & Vig, L. (2021) · Machine Learning · doi:10.1007/s10994-021-05966-z · Cited by 30
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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
About these checks
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