Adaptive Automated Scientific Laboratory

Our proposal integrates the scientific method with 21st century automation technology, with the goal of making scientific discovery more efficient (cheaper, faster, better). A "Robot Scientist" is a physically implemented laboratory automation system that exploits techniques from the field of artificial intelligence to execute cycles of scientific experimentation. Our vision is that within 10 years many scientific discoveries will be made by teams of human and robot scientists, and that such collaborations between human and robot scientists will produce scientific knowledge more efficiently than either could alone. In this way the productivity of science will be increased, leading to societal benefits: better food security, better medicines, etc. The Physics Nobel Laureate Frank Wilczek has predicted that the best scientist in one hundred years time will be a machine. The proposed project aims to take that prediction several steps closer.

We will develop the AdaLab (an Adaptive Automated Scientific Laboratory) framework for semi-automated and automated knowledge discovery by teams of human and robot scientists. This framework will integrate and advance a number of ICT methodologies: knowledge representation, ontology engineering, semantic technologies, machine learning, bioinformatics, and automated experimentation (robot scientists). We will evaluate the AdaLab framework on an important real-world application in cell biology with biomedical relevance to cancer and ageing. The core of AdaLab will be generic.

The expected project outputs include:

- An AdaLab demonstrated to be greater than 20% more efficient at discovering scientific knowledge (within a limited scientific domain) than human scientists alone.
- A novel ontology for modelling uncertain knowledge that supports all aspects of the proposed AdaLab framework.
- The first ever communication mechanism between human and robot scientists that standardises modes of communication, information exchange protocols, and the content of typical messages.
- New machine learning methods for the generation and efficient testing of complex scientific hypotheses that are twice as efficient at selecting experiments as the best current methods.
- A significant advance in the state-of-the-art in automating scientific discovery that demonstrates its scalability to problems an order of magnitude more complex than currently possible.
- Novel biomedical knowledge about cell biology relevant to cancer and ageing.
- A strengthened interdisciplinary research community that crosses the boundaries between multiple ICT disciplines, laboratory automation, and biology.

All outputs produced by the project will be made publicly available by the end of the project.

The proposed project has high potential for significant technological, economical, and societal impacts. This potential impact of robot scientists has been widely recognised, e.g. in the Nature editorial of 15.1.04 their potential synergistic collaboration with human scientists is stressed, "an automated system that designs its own experiments will benefit young molecular geneticists"; a reviewer of our article in Science (King et al, 2009) stated that the work was of "historical significance"; and Time magazine named it the 4th most significant scientific advance of 2009. The proposed project principally extends the previous work by developing a (semi-) automated framework for scientific discoveries by teams of human and robot scientists (AdaLab), making it far more applicable to general biomedical research.

The AdaLab framework will contribute to realising Europe's 2020 strategy for smart, sustainable and inclusive growth. The project would contribute to the future development of (semi)-automated laboratories across Europe and wider. These intelligent laboratories have the potential to speed up the technological progress. Our cautious estimate is that the exploitation of the AdaLab framework in scientific laboratories would increase the efficiency of laboratory experimentation by 20% (see section 2). Such an increase would lead to more scientific discoveries, better technological solutions, and new products.

Science is the greatest generator of economic wealth (through developments in technology), and the greatest driver of better health (through development in biomedical science). Therefore all EU citizens will potentially benefit from the proposed research. For example new better drugs could be delivered to the market faster and cheaper. Currently, ~25Billion euros is spent annually within the EU on pharmaceutical research. Most of this is spent on late-stage trials (which are less amenable to automation), but conservatively estimating that 10% is amenable to the AdaLab framework, then a 20% efficiency gain would result in savings of ~0.5Billion euros per annum.