Large-scale biological studies often require researchers to process hundreds or thousands of samples, making manual laboratory work time-consuming and difficult to scale. A more automated system could allow researchers to analyze larger numbers of samples while maintaining consistent methods and data quality. To support this, the project is developing an automated platform for multi-omics research, which combines different types of biological information, including microbial DNA and metabolites.
The platform brings robotic equipment, sample preparation, DNA extraction, sequencing preparation, and data management into a single automated workflow. The team is testing the system for microbial DNA analysis while also developing methods to study metabolites and manage laboratory data. Although initially designed for human microbiome research, its flexible design may allow it to handle other types of samples. It is now being tested with soil samples and may later be adapted to analyze environmental DNA in water. Together, these tools are intended to create AI-ready research infrastructure that can process large numbers of samples with minimal human intervention.
By reducing manual laboratory work, the platform could make large-scale studies in health and environmental research more efficient and consistent. This research is part of the MANTA Project, which represents Phase 1 of the Nobel Turing Challenge led by Professor Hiroaki Kitano. The goal of this phase is to establish a platform for autonomous biological data collection and generation. In the longer term, the platform is intended to provide the AI-ready research infrastructure needed to develop an “AI Scientist” capable of conducting scientific research with a high degree of autonomy and minimal human intervention.
