A major goal in engineering of biological systems is to predictably reprogram the genetics of cells to control a cellular function, such as to produce readily detectable indicators of metabolic states or environmental conditions. Although recent years have seen numerous advances towards this goal, the sophistication, performance and scale of mammalian genetic circuits trail their microbial counterparts. In this collaborative project, investigators from the US (Boston University) and the UK (University of Warwick) combine experimentation and computational modeling to develop a new genetic technology tool that will enable mammalian cells to respond to a signal to reprogram genetic circuits with memory capabilities. The memory feature is crucial to enable recordings of past events in complex environments where continuous monitoring is not feasible, which will be potentially useful for biopharmaceutical production. This project provides interdisciplinary training opportunities for high school, undergraduate, and graduate students in quantitative experimental methods and cell biology.
This project will develop new tools for controlling mammalian cell gene expression using a class of DNA modifying enzymes called serine integrases. Serine integrases have advantages over the better characterized tyrosine recombinases due to the presence of recombinase directionality factors that bind to serine integrases to modify its conformation, permitting the reverse reaction to occur. To achieve this goal the PIs will: 1) perform detailed analyses of serine integrase (Bxb1, PhiC31, TP901-1 and TG1) activity and specificity in mammalian cells; 2) design, build and test inducible, rewritable memory switches for biopharmaceutical production; and 3) design, construct, and test a serine recombinase-based genetic circuit with reversible memory capabilities. This work will provide in-depth characterization of the specificity and toxicity of serine integrases and develop a novel decoder system for the reversible genetic circuit through integrated experimentation and computational modeling.
This collaborative US/UK project is supported by the US National Science Foundation and the UK Biotechnology and Biological Sciences Research Council.
