I-Corps: Translation Potential of a Cloud Laboratory Platform for Laboratory Automation

This I-Corps project focuses on the development of automated laboratories for the remote execution of scientific experiments. Automated laboratories allow scientists to submit and manage their experiments over the internet, eliminating the need for a physical presence in a traditional laboratory setting. Currently, traditional laboratories involve significant upfront investments in equipment, resources, and dedicated laboratory spaces, making research costly and limiting accessibility, especially for smaller research teams and educational institutions. By removing these barriers, automated laboratories significantly reduce the cost and complexity of conducting scientific research, enabling participation from researchers in academia, startups, and industry. The widespread adoption of automated laboratories would enhance the reproducibility of scientific research, accelerate innovation across the life sciences, and support national interests by facilitating advancements in healthcare, biotechnology, and pharmaceuticals.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a cloud-based laboratory platform integrating robotics, automation software, and artificial intelligence-driven systems. The core of this technology involves a programming language specifically designed to describe detailed experimental protocols, which the platform translates into precise instructions executed by robotic instruments and laboratory personnel. Users benefit from unprecedented accuracy, reproducibility, and data transparency, greatly facilitating analysis and collaboration. Furthermore, the integration of advanced artificial intelligence models aids researchers in experimental design, result interpretation, and logistical management. Adoption of this technology will enable researchers to scale their experiments rapidly, conduct parallel experiments efficiently, and significantly streamline the path from hypothesis to discovery.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Award Number: 2521791
Principal Investigator: Mohammed AlQuraishi
Funds Obligated: $50,000
State: NY