This application addresses broad Challenge Area (06) Enabling Technologies and Specific Challenge Topic, 06-CA-103: Synthetic Biology. With advances in our understanding of the underlying molecular mechanism of diseases such as cancer, new genetically encoded technologies that support the targeted reprogramming of cellular fate and function in humans are critical to the development of next-generation disease treatment strategies. While newer therapeutic approaches are based on modifying the activities of specific genetic targets, the ability to precisely control the therapeutic effect of these strategies in response to specified molecular signals will result in safer, more effective, and targeted strategies in treating disease. Due to the diversity of genetic targets, pathways, and markers associated with different diseases, the ability to rapidly and efficiently tailor the input/output functions of biological information processing and control systems is important to the successful implementation and broader translation of such targeted disease treatment strategies. The project will address technical challenges in the development of a set of enabling technologies that will form the foundation of a new genetically encoded technology that supports the efficient programming of cellular information processing and control functions, thereby addressing current limitations and inefficiencies in biological design strategies. The long-term implications of the research will address current limitations in our ability to transmit information to and from living systems and implement control within cells themselves, thereby broadly transforming how we interact with and program biology. The goals of this project are to develop molecular platforms for the rapid and efficient programming of RNA molecules that encode integrated sensing and actuation, information processing, and dynamic control functions to engineer cellular fate and function. Our synthetic biology approach is grounded in both biology and engineering foundations and based on unique advantages of RNA as a design substrate for genetically encoded molecular sensing-regulatory tools for applications in health and medicine.
Specific aims are to: 1. Develop experimental frameworks for the rapid and scalable design of RNA-based sensing- actuation systems in mammalian cells. 2. Develop high-throughput strategies for the generation of new RNA component functions. 3. Develop computational frameworks for the in silico design and optimization of RNA-based sensing-actuation systems. 4. Apply the developed RNA-based platform to the initial design and implementation of targeted molecular therapy strategies. This research will result in a new genetically encoded technology that will support the targeted reprogramming of cellular fate and function, providing the foundations for the development of safer, more effective, targeted disease treatment strategies. A set of enabling technologies will be developed that will address current challenges in the design of new biological functions, allowing the rapid design and implementation of molecules that encode user-specified sensing, regulatory, and signal processing functions. In addition, this research will have broader effects on our ability to interface with and program biology, thereby transforming basic and applied biomedical research and biotechnology.
This research will result in a new genetically encoded technology that will support the targeted reprogramming of cellular fate and function, providing the foundations for the development of safer, more effective, targeted disease treatment strategies. A set of enabling technologies will be developed that will address current challenges in the design of new biological functions, allowing the rapid design and implementation of molecules that encode user-specified sensing, regulatory, and signal processing functions. In addition, this research will have broader effects on our ability to interface with and program biology, thereby transforming basic and applied biomedical research and biotechnology.
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