| NSF Org: |
EF Emerging Frontiers |
| Recipient: |
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| Initial Amendment Date: | August 14, 2019 |
| Latest Amendment Date: | July 20, 2021 |
| Award Number: | 1935087 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Charles Cunningham
chacunni@nsf.gov (703)292-2283 EF Emerging Frontiers BIO Directorate for Biological Sciences |
| Start Date: | September 15, 2019 |
| End Date: | August 31, 2024 (Estimated) |
| Total Intended Award Amount: | $1,000,000.00 |
| Total Awarded Amount to Date: | $1,022,500.00 |
| Funds Obligated to Date: |
FY 2020 = $392,277.00 FY 2021 = $22,500.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
4333 BROOKLYN AVE NE SEATTLE WA US 98195-1016 (206)543-4043 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4333 Brooklyn Ave NE Seattle WA US 98195-1750 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
URoL-Understanding the Rules o, Cellular Dynamics and Function, Cellular & Biochem Engineering, Systems and Synthetic Biology |
| Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT 01002122DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.074 |
ABSTRACT
Adaptation is a fundamental and defining feature of biology. In principle, living systems can adapt via two mechanisms: evolution and learning. Learning is a potentially rapid and powerful mode of adaptation. Even the simplest cells can evolve, but can they demonstrate learning in the absence of evolution? If so, what modes of learning can they engage in, and how simple can learning cells or cell-like systems be? In setting out to address these fundamental questions about the Rules of Life, this project will help to define the essential biological nature of learning systems. This project will make important progress towards the bottom-up construction of 'smart' synthetic cell systems, with potential future applications across a wide range of academic, industry, clinical and environmental settings. A multi-disciplinary cohort of graduate students will be recruited and trained in interdisciplinary research, and a set of 'science & society' modules for bioengineering-related courses will be developed. Furthermore, the project will engage a broader public audience by developing hands-on activities related to the goals of the project.
This project aims to create synthetic cell systems capable of associative learning. Specifically, the project will develop a synthetic cell that learns to respond to a light pulse signal by associating it with the addition of molecules detected by olfactory receptors. Success will provide a proof-of-principle that genetically encoded information-processing systems can carry out learning tasks, and will generate a reusable library of learning circuit motifs. Modeling and design of associative learning circuits will inform the development of corresponding genetic regulatory circuit architectures. Multi-input chemical signals will be sensed using a library of olfactory receptor proteins, and the effects of membrane encapsulation on system behavior will be studied. Finally, an integrated Human Practices component will explore the relationship between learning synthetic cells and artificial neural networks/machine learning, from historical, conceptual and ethical perspectives.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The goal of this project was to develop synthetic cell systems capable of associative learning. Studies provided a proof-of-principle that genetically encoded information-processing systems can carry out learning tasks, and generated reusable learning circuit motifs. Biophysical effects on macromolecules encapsulated in synthetic cells were described. Circuit motifs for learning and information storage, and approaches to control genetic networks, were studied.
This project has taken important steps toward developing molecular recording technologies that can store information about cellular events and transform them into programmable responses. Circuitry that is responsive to the timing, order, and intensity of molecular events can give engineers control over how a bacterial cell processes and transforms information into downstream responses. Such capabilities will enhance our ability to engineer microbes for next-generation bioproduction applications.
This work contributed to the efforts in synthetic biology to control and engineer artificial cells for a variety of applications in biotechnology. Data and results were shared in publically accessible scientific journals and repositories. The project trained undergraduate students, graduate students, and postdoctoral fellows in the field of synthetic biology, who have subsequently entered the U.S. workforce in positions in industry, academia, and government agencies. The project has also produced tabletop public demonstration activities and teaching resources for synthetic biology courses.
Last Modified: 04/17/2025
Modified by: James Carothers
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