King Ross D, Whelan Kenneth E, Jones Ffion M, Reiser Philip G K, Bryant Christopher H, Muggleton Stephen H, Kell Douglas B, Oliver Stephen G
Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK.
Nature. 2004 Jan 15;427(6971):247-52. doi: 10.1038/nature02236.
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.
科学过程是否能够自动化这一问题，既具有重大的理论意义，又在实践中愈发重要，因为在许多科学领域，数据的产生速度远远超过了有效分析的速度。我们描述了一个实际应用的机器人系统，该系统运用人工智能技术来进行科学实验循环。该系统会自动提出假设以解释观测结果，设计实验来检验这些假设，使用实验室机器人实际开展实验，解读实验结果以证伪与数据不符的假设，然后重复这个循环。在此，我们将该系统应用于利用酵母（酿酒酵母）缺失突变体和营养缺陷型生长实验来确定基因功能。我们构建并测试了一个关于芳香族氨基酸合成途径的详细逻辑模型（涉及基因、蛋白质和代谢物）。在自动重建该模型部分内容的生物学实验中，我们表明一种智能实验选择策略与人类表现相当，并且显著优于最便宜和随机实验选择，成本分别降低了3倍和100倍。
