Mammalian synthetic biology aims to rationally program the behavior of cells with synthetic molecular circuits, and it holds great promises for diverse biomedical fields such as cell fate reprogramming and oncolytic virology. Synthetic circuits have been predominantly constructed with transcription factors, and delivered on DNA-based vectors that are compatible with transcriptional regulation but may insert into and mutagenize the host genome. Protein-level circuits would potentially operate faster, compute in parallel in subcellular compartments, and interface directly with cell endogenous inputs/outputs. They would also enable the development of RNA-based vectors with lower mutagenic risks, because protein-level circuits can serve as both cargos that functions properly even when expressed from an RNA vector, and as controllers for RNA viruses through regulation of essential viral proteins. However, despite researchers' efforts, protein circuits have been limited to a few ad hoc examples, because existing protein components, unlike transcriptional units, lack composability (the ability to select and assemble modular building blocks differently for different tasks). In our preliminary study, we successfully engineered viral proteases as composable elements for protein-level circuits, and demonstrated a broad variety of functions. Building upon the initial success, I will enhance the capability of protease circuits, and concurrently develop a RNA vector for their delivery. To better couple protease circuits to cell endogenous inputs/outputs, I will build detection modules that converts specific proteins' presence and signaling events into protease activity, and execution modules that knock down endogenous proteins. To meet a larger input/output palette with expanded computing capacity, I will mine more orthogonal proteases and engineer them to be regulatable by other proteases. Meanwhile, to facilitate the optimization of more complex circuits and to explore its unique features compared to traditional transcriptional circuits, I will establish a computational framework for simulating protease circuits. As for delivery, I will engineer a negative-strand RNA virus into a vector by regulating essential viral proteins with protease circuits to achieve safety and cell-type specificity. I will also test the limit of the vector's cargo capacity and explore strategies to raise the limit. All told, my project will engender a more powerful platform for constructing and delivering DNA-free synthetic circuits into mammalian cells. My career goal is to lead my independent research group devoted to establishing a general-purpose toolkit for non-mutagenic manipulation of mammalian cells. During the K99 phase, I will continue to receive in- depth quantitative and mathematical training from Dr. Elowitz and our collaborators, and acquire key experimental techniques from my consultants. I will also have demonstrated the feasibility of the basic designs behind each aim. My trained skills, as well as individual designs, will come together in the R00 phase, give rise to more complex circuits with direct biomedical relevance, and lay the foundation for my future career.

Public Health Relevance

This proposal aims to develop a new platform for the construction and delivery of protein-level circuits that program the behavior of mammalian cells while avoiding mutagenizing the host genome. This platform will facilitate diverse biomedical applications wherever it is necessary to eliminate or fix pathological cells, or generate new ones on demand in human patients. Specific examples include a ?smart? virus that senses intracellular hallmarks and lyses cancer cells, and one that responds to different stages of cell fate reprogramming and dynamically and efficiently ushers cells through this process in vivo.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Transition Award (R00)
Project #
Application #
Study Section
Special Emphasis Panel (NSS)
Program Officer
Rampulla, David
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code