I-Corps: Translation Potential of an Integrated Miniaturized Laboratory System

This I-Corps project focuses on the potential commercialization of a miniaturized, integrated laboratory system that enhances tissue culture and preclinical research. This technology enables automated biological experimentation by combining microfluidics, imaging, automation, and electrophysiology within a cloud-connected framework. By improving experimental reproducibility, minimizing tissue disruption, and stabilizing cultures, the system addresses critical challenges in biological research. The platform facilitates remote access to experiments and standardized protocol sharing, promoting global scientific collaboration. The solution lowers barriers to advanced research techniques, making them more accessible to additional laboratories. The system's ability to generate physiologically relevant data enhances understanding of human biology, advancing drug discovery while reducing the reliance on animal testing. By automating complex biological workflows, the technology accelerates therapeutic development and enhances preclinical drug testing reliability. This innovation also supports workforce development by introducing user-friendly interfaces that enable training in advanced research techniques without requiring extensive technical expertise. By fostering accessibility, collaboration, and data-driven scientific advancements, this project contributes to the broader goal of improving human health through next-generation research tools. 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 an automated tissue culture system that integrates microfluidics, real-time imaging, and electrophysiological monitoring within a cloud-connected environment. By seamlessly combining biological experimentation with engineering advances, the system enables continuous, high-throughput data collection while minimizing manual intervention. The modular design allows for customization across various research applications, including drug discovery and disease modeling. Advanced data analytics, powered by artificial intelligence, provide deeper insights into drug-tissue interactions and cellular responses. This approach enhances experimental reproducibility, reduces variability, and improves the predictive accuracy of preclinical research. The integration of cloud computing and remote accessibility expands the potential user base, ensuring the technology can be widely adopted across research institutions and industry sectors. By improving data quality, automating workflows, and enabling real-time experimental monitoring, this innovation represents a significant step forward in preclinical research methodologies and therapeutic development. 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.