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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Massachusetts Institute of Technology |
| Country | United States |
| Start Date | Jun 01, 2025 |
| End Date | May 31, 2026 |
| Duration | 364 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2524772 |
This I-Corps project focuses on the development of affordable and widespread access to cell therapies. Current cell therapies are already extremely effective at treating certain cancers, with 85% remission for liquid tumors. However, manufacturing complexity and the high price of necessary raw biologic materials drive up the price, with less than 7% of eligible patients receiving this lifesaving treatment.
This technology enables autonomous multi-step differentiation of stem cells into therapeutic cells, eliminating the need for constant monitoring by skilled scientists and expensive biologics during production. This innovation reduces overall costs by over 50%, enabling widespread adoption of numerous cell therapies. Current cell therapy manufacturing demand is valued at $22.5 billion.
As cell therapies address more diseases, the manufacturing bottleneck is going to extend to applications in solid tumors, regenerative medicine, aging, biotechnology research, and synthetic biology.
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 artificial neural networks within living cells to enable precise control of cellular behavior. The technology includes two key innovations.
First, it implements analog computation in cells through silencing-resistant biological circuits that mimic neural network architectures, allowing cells to process complex information and make autonomous decisions. Second, it leverages a novel artificial intelligence (AI) architecture that can both predict circuit behavior and generatively design new circuits to achieve desired cellular functions.
The approach has been shown to enable sophisticated control of cell state transitions while maintaining long-term circuit stability. Initial results demonstrate a 50% improvement in yield for stem cell differentiation into therapeutically relevant cells compared to conventional approaches.
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.
Massachusetts Institute of Technology
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