Functional genomic hypothesis generation and experimentation by a robot scientist Export

@article{citeulike:292, abstract = {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.}, address = {Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK.}, author = {King, Ross D. and Whelan, Kenneth E. and Jones, Ffion M. and Reiser, Philip G. K. and Bryant, Christopher H. and Muggleton, Stephen H. and Kell, Douglas B. and Oliver, Stephen G.}, citeulike-article-id = {292}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nature02236}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nature02236}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/14724639}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=14724639}, day = {15}, doi = {10.1038/nature02236}, issn = {1476-4687}, journal = {Nature}, keywords = {agi, ai}, month = {January}, number = {6971}, pages = {247--252}, posted-at = {2007-08-11 01:19:38}, priority = {3}, title = {Functional genomic hypothesis generation and experimentation by a robot scientist}, url = {http://dx.doi.org/10.1038/nature02236}, volume = {427}, year = {2004} }

TY - JOUR ID - citeulike:292 L3 - citeulike-article-id:292 N2 - 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. IS - 6971 JF - Nature SN - 1476-4687 AD - Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK. EP - 252 TI - Functional genomic hypothesis generation and experimentation by a robot scientist VL - 427 SP - 247 KW - agi KW - ai AU - King, Ross AU - Whelan, Kenneth AU - Jones, Ffion AU - Reiser, Philip AU - Bryant, Christopher AU - Muggleton, Stephen AU - Kell, Douglas AU - Oliver, Stephen PY - 2004/01/15/ UR - http://dx.doi.org/10.1038/nature02236 ER -