Team:BostonU/Backbones

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Lower Copy Backbones

Why Lower Copy Count Origins of Replication?

The work carried out by previous BU iGEM teams used destination vectors, or plasmid backbones, with high-copy origins of replication. These high-copy plasmids in our library would not allow for optimal performance of our multiplexed transcriptional units and larger constructs. Additionally, works upon which we are basing our complex circuit assembly do not use high copy origins in their devices due to this loss of correct functionality. Circuit behavior would be desynchronized with the presence of a high copy origin, as it causes overexpression of the plasmid in a cell, leading to a high amount of transcription and protein expression. For more complex circuits, this overexpression pushes the limit of the amount of ribosomes that can be sequestered for translation, in addition to straining the cell's protein degradation mechanisms.

Published complex genetic circuits, for example the NOR gates [1] and the Collins counter [2], are often in cloned in medium or low copy vectors, using the ColE1 or p15A origin of replication, respectively. The plasmid maps shown below are from those papers and show the origins of replication used for their work.

The image above shows two plasmid maps from the NOR paper [1] (right and middle) and one of the counters [2] (left). These were obtained from the Supplemental Information for both publications.


Detailed progress on new vector backbone creation can be found in the backbones notebook.

Design and Assembly





Schematic of origin cloning - The PCR primer design added restriction sites for the MfeI and AscI restriction enzymes, which would give the ends of each of the amplified fragments compatible 4bp overhangs suitable for ligation. (Detailed primer design available here).


Testing

Flow cytometry testing of new low copy backbones has been carried out for the p15A origin against the pMB1 origin, using the flow cytometry controls.


References


[1] A. Tamsir, J. Tabor, C. Voigt (2011). “Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’.” Nature 469: 212-215
[2] A. Friedland, T. Lu, X. Wang, D. Shi, G. Church, J. Collins (2009). "Synthetic Gene Networks That Count." Science 324:1199-1202 DOI: 10.1126/science.1172005







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