The project focused on developing and optimizing cell-free computing circuits and programmable responsive elements for biosensor design. In the first work package, the team successfully prepared functional cell lysate at TU/e, producing green fluorescent protein (GFP) and initiating the cloning of plasmids for cell-free expression of CRISPR-Cas-based riboregulators. They also developed a machine learning model for optimizing cell-free gene expression. Another key achievement was setting up a genetic system to screen intein activity, utilizing split inteins that splice proteins and serve as components in biosensor circuits.
RNA-encoded regulatory circuits were explored, particularly the RNA-binding protein MS2-CNOT7, for detecting protease activity. This system showed promise as a biosensor component, though it did not repress translation in cell-free systems, and the RNA de-adenylation process was deemed unsuitable for biosensors. Additionally, DNA origami was designed, produced, and characterized to facilitate the co-localization of phosphorylation cascades and riboregulators, confirmed through gel electrophoresis, atomic force microscopy, and fluorescence microscopy.
In the second work package, the team aimed to achieve temporal control of in vitro transcription using intrinsically disordered nucleic acids. They developed a method for real-time monitoring of transcription and devised a strategy for introducing a tunable delay in the transcription reaction, enhancing the control over in vitro transcription processes.
