Andrew Charles Sparkes

An ontology for a Robot Scientist

MOTIVATION A Robot Scientist is a physically implemented robotic system that can automatically carry out cycles of scientific experimentation. We are commissioning a new Robot Scientist designed to investigate gene function in S. cerevisiae. This Robot Scientist will be capable of initiating >1,000 experiments, and making >200,000 observations a day. Robot Scientists provide a unique test bed for the development of methodologies for the curation and annotation of scientific experiments: because the experiments are conceived and executed automatically by computer, it is possible to completely capture and digitally curate all aspects of the scientific process. This new ability brings with it significant technical challenges. To meet these we apply an ontology driven approach to the representation of all the Robot Scientists data and metadata. RESULTS We demonstrate the utility of developing an ontology for our new Robot Scientist. This ontology is based on a general ontology of experiments. The ontology aids the curation and annotating of the experimental data and metadata, and the equipment metadata, and supports the design of database systems to hold the data and metadata. AVAILABILITY EXPO in XML and OWL formats is at: http://sourceforge.net/projects/expo/. All materials about the Robot Scientist project are available at: http://www.aber.ac.uk/compsci/Research/bio/robotsci/.

Towards Robot Scientists for autonomous scientific discovery

We review the main components of autonomous scientific discovery, and how they lead to the concept of a Robot Scientist. This is a system which uses techniques from artificial intelligence to automate all aspects of the scientific discovery process: it generates hypotheses from a computer model of the domain, designs experiments to test these hypotheses, runs the physical experiments using robotic systems, analyses and interprets the resulting data, and repeats the cycle. We describe our two prototype Robot Scientists: Adam and Eve. Adam has recently proven the potential of such systems by identifying twelve genes responsible for catalysing specific reactions in the metabolic pathways of the yeast Saccharomyces cerevisiae. This work has been formally recorded in great detail using logic. We argue that the reporting of science needs to become fully formalised and that Robot Scientists can help achieve this. This will make scientific information more reproducible and reusable, and promote the integration of computers in scientific reasoning. We believe the greater automation of both the physical and intellectual aspects of scientific investigations to be essential to the future of science. Greater automation improves the accuracy and reliability of experiments, increases the pace of discovery and, in common with conventional laboratory automation, removes tedious and repetitive tasks from the human scientist.

Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist ‘Eve’ designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.

Yeast-based automated high-throughput screens to identify anti-parasitic lead compounds

We have developed a robust, fully automated anti-parasitic drug-screening method that selects compounds specifically targeting parasite enzymes and not their host counterparts, thus allowing the early elimination of compounds with potential side effects. Our yeast system permits multiple parasite targets to be assayed in parallel owing to the strains’ expression of different fluorescent proteins. A strain expressing the human target is included in the multiplexed screen to exclude compounds that do not discriminate between host and parasite enzymes. This form of assay has the advantages of using known targets and not requiring the in vitro culture of parasites. We performed automated screens for inhibitors of parasite dihydrofolate reductases, N-myristoyltransferases and phosphoglycerate kinases, finding specific inhibitors of parasite targets. We found that our ‘hits’ have significant structural similarities to compounds with in vitro anti-parasitic activity, validating our screens and suggesting targets for hits identified in parasite-based assays. Finally, we demonstrate a 60 per cent success rate for our hit compounds in killing or severely inhibiting the growth of Trypanosoma brucei, the causative agent of African sleeping sickness.

The Robot Scientist Adam

Despite sciences great intellectual prestige, developing robot scientists will probably be simpler than developing general AI systems because there is no essential need to take into account the social milieu.

THE ANALYSIS OF YEAST CELL MORPHOLOGY FEATURES IN EXPONENTIAL AND STATIONARY PHASE

The use of automated microscopes, combined with digital image analysis, is an increasingly important way of high-throughput phenotype analysis of biological systems. We have developed a new method of measuring the basic morphological features of budding yeast (Saccharomyces cerevisiae) cells. Using this method we have performed investigation on four deletant strains: ΔYLR371w, ΔYDR349c, ΔYLR192c, and ΔYDR414c. These investigations demonstrate that our robotics and image analysis software provide an efficient way to automatically obtain quantitative morphology features of yeast cells. The results show that statistically significant morphological differences can be identified between strains, and that these differences vary by growth stage.

Make way for robot scientists.

In their 19 June letter (“Machines fall short of revolutionary science,” p. [1515][1]), P. W. Anderson and E. Abrahams, commenting on our work on the automation of science, state that we are “seriously mistaken about the nature of the scientific enterprise.” Their argument seems to be based

AutoLabDB: a substantial open source database schema to support a high-throughput automated laboratory

MOTIVATION Modern automated laboratories need substantial data management solutions to both store and make accessible the details of the experiments they perform. To be useful, a modern Laboratory Information Management System (LIMS) should be flexible and easily extensible to support evolving laboratory requirements, and should be based on the solid foundations of a robust, well-designed database. We have developed such a database schema to support an automated laboratory that performs experiments in systems biology and high-throughput screening. RESULTS We describe the design of the database schema (AutoLabDB), detailing the main features and describing why we believe it will be relevant to LIMS manufacturers or custom builders. This database has been developed to support two large automated Robot Scientist systems over the last 5 years, where it has been used as the basis of an LIMS that helps to manage both the laboratory and all the experiment data produced.

An Integrated Laboratory Robotic System for Autonomous Discovery of Gene Function

Progress in laboratory automation depends not only on automating the physical aspects of scientific experimentation, but also on the intellectual aspects. We present the conceptual design, implementation, and our user-experience of “Adam,” which uses machine intelligence to autonomously investigate the function of genes in the yeast Saccharomyces cerevisiae. These investigations involve cycles of hypothesis formation, design of experiments to test these hypotheses, physical execution of the experiments using laboratory automation, and the analysis of the results. The physical execution of the experiments involves growing specific yeast strains in specific media and measuring growth curves. Hundreds of such experiments can be executed daily without human intervention. We believe Adam to be the first machine to have autonomously discovered novel scientific knowledge.

The Analysis of Yeast Cell Morphology Using a Robot Scientist

We have developed image analysis methods to analyse the morphology of the budding yeast (Saccharomyces cerevisiae) cell. Experiments were performed on four deletant strains: DYLR371w, DYDR349c, DYLR192c, and DYDR414c. Our results show that our image analysis software provides an efficient way to automatically obtain quantitative morphology features of yeast cells. Our research show significant differences from those previously published for these strains. These differences may be due to different growth conditions or the use of unfixed cells. More research is required to understand the complex relationship between genotype and environment in yeast morphology.