NSF requires disclosure of AI tool usage in proposal preparation. Ensure you disclose the use of FindGrants' AI drafting in your application.
NSF
Synthetic biology can be used to create and improve treatments for many diseases. Genetic techniques are used to modify T cells to create CAR-T cells that can kill some types of cancer cells. Gene therapy offers the promise of curing genetic diseases for which there are no treatments. A significant problem with these technologies is the variability of gene delivery to the targeted cells. Viral particles are often used to deliver the desired genes. A cell could be infected by a single viral particle, or many, meaning the cell could receive one or many copies of the gene to be expressed. The variability in the resulting response of the infected cells means the treatment results could vary widely. The objective of this project is to develop protein circuits that can self-regulate. This would remove the effect of infection variability on cellular performance, and thereby even out the therapeutic effectiveness. The reproducibility this would introduce would accelerate the development process for new biotherapeutic strategies. This project will develop dosage-controlled synthetic circuits by implementing proteolysis-based incoherent feedforward loops (IFFLs). The primary goal is to create self-regulating circuits that maintain consistent performance regardless of delivery variations. The approach combines proteolytic regulation with secreted protein engineering. The research will proceed in two phases: first, establishing the technical foundation using synthetic reporters to develop and characterize the basic circuit components, followed by demonstrating functional feasibility using cytokine outputs. An iterative strategy of computational modeling and experimental validation to optimize circuit design and performance will be employed. The methodology includes developing proteolytic control mechanisms, engineering secreted protein systems, and implementing circuit-on-circuit dosage control through careful characterization and tuning of individual components. The experimental approach will systematically progress through several key stages. Initially, individual proteolytic regulatory elements will be designed and optimized, establishing their kinetic parameters and dose-response characteristics. These components will then be integrated into IFFL circuits, carefully validating their function using fluorescent reporters. Computational modeling will guide the design process and help predict circuit behavior under various conditions. The project will advance to testing with therapeutic proteins, specifically cytokines, as output molecules. This phase will include extensive characterization of circuit performance in therapeutically relevant cell types and delivery vectors. 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.
Up to $300K
2027-08-31
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.
One-time $749 fee · Includes AI drafting + templates + PDF export
EPSCoR CREST Phase I: Center for Post-Transcriptional Regulation
NSF — up to $7.5M
CREST Phase I: Center for Circadian Rhythmicity and Sleep Homeostasis
NSF — up to $7.4M
Institute for Foundations of Machine Learning
NSF — up to $6.5M
MIP: Biomaterials, Polymers, and Advanced Constructs from Integrated Chemistry Materials Innovation Platform (BioPACIFIC MIP)
NSF — up to $5.8M
A Shallow Drilling Campaign to Assess the Pleistocene Hydrogeology, Geomicrobiology, Nutrient Fluxes, and Fresh Water Resources of the Atlantic Continental Shelf, New England
NSF — up to $5.0M
BII: Predicting the global host-virus network from molecular foundations
NSF — up to $4.8M