Synthetic regulatory circuits for programmable control of cell physiology
The newly emerging discipline of Synthetic Biology holds the promise of radically changing the way we probe, control and augment living matter from single cells to entire organisms, and revolutionize basic biological research, bio...
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BFU2015-66894-P
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Descripción del proyecto
The newly emerging discipline of Synthetic Biology holds the promise of radically changing the way we probe, control and augment living matter from single cells to entire organisms, and revolutionize basic biological research, biotechnology, and medicine. However, practical work toward these important goals is still in its infancy, in part because concrete approaches to achieve rational control of cell physiology are currently lacking. In order to advance this vision, here we propose a detailed strategy toward engineered regulatory circuits that read out complex cellular states based on multiple biological signals, and convert this information into a desired action based on pre-programmed signal integration. If successful, our strategy will enable unprecedented level of rational intervention with the cell.
Specifically, we suggest to read out cellular information as relayed by expression and activity of cell’s transcription factors, proteins that control gene expression and serve as major regulators of cell fate and cell response to transient stimuli. The readout will be accomplished with the help of specially-designed sensor promoters that will in turn drive the expression of engineered microRNA molecules. Those molecules in turn will converge on a small number of response elements in engineered downstream transcripts, implementing highly-flexible and programmable logic integration of the original transcription factor signals (Rinaudo et al, Nature Biotechnology, 2007 and Leisner et al, Nature Nanotechnology, 2010).
We propose a stepwise bottom-up construction strategy whereby we first design, test and optimize sensor promoters for individual TFs, next we integrate them into large networks, and finally we show how to utilize these networks as prototype selective anti-cancer therapies. To validate our approaches, we will use human cancer cell lines as a model system.