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UID:968@biotech.technion.ac.il
DTSTART;TZID=Asia/Jerusalem:20201028T160000
DTEND;TZID=Asia/Jerusalem:20220202T164952
DTSTAMP:20220512T124720Z
URL:https://biotech.technion.ac.il/events/overcoming-the-design-build-test
 -bottleneck-for-synthesis-of-rna-binding-cassettes-2/
SUMMARY:Overcoming the design\, build\, test bottleneck for synthesis of RN
 A binding cassettes
DESCRIPTION:Biological circuits in synthetic biology are designed to perfor
 m logical functions\, similar to their electrical counterparts\, inside li
 ving cells. However\, unlike electronic parts\, biological parts are often
  noisy and error-prone\, requiring many iterations of trial and error\, te
 rmed the design-build-test (DBT) cycle\, before a successful design is ach
 ieved. These cycles are a major bottleneck for progress in the field. The 
 emergence of high-throughput experimental techniques combined with machine
  learning (ML) algorithms\, provide the ingredients for a potential “big
 -data” solution that can generate predictive capability to overcome the 
 DBT bottleneck. In this work\, we apply such an approach to the design of 
 RNA cassettes used in gene editing and RNA tracking systems. RNA cassettes
  are typically made of repetitive hairpin-structured binding sites\, there
 fore hindering their retention\, synthesis\, and functionality. Here\, we 
 carried out an oligo-library-based experiment to generate thousands of new
  binding sites for the phage coat proteins (CP) of bacteriophages MS2\, PP
 7\, and Qβ. We then applied a neural network to study and predict protein
  binding. We then designed new predicted non-repetitive cassettes and vali
 dated their functionality in mammalian cells. Taken together\, we develope
 d a tool for improving imaging experiments\, with more robust measurements
  and quantitative data.\n\n \n\nAbstract
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