Site-specific recombination (SSR), found mostly in bacteria, viruses and transposons (the so-called parasitic DNA), is one of the many mechanisms which life utilises to perform genetic recombination. SSR generally consists of a recombinase protein that mediates recombination, and two DNA elements, called target sites, that are similar or identical to each other that the recombinase recognises. Depending on the orientation and the location of the target sites, SSR can perform DNA integration, excision, and exchange. While bacteria use SSR to regulate gene expression and separate two chromosomes during cell division, viruses and transposons use it to mediate chromosomal integration into the bacterial chromosome, hijacking the cellular machinery to replicate themselves.
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We believe that mathematical modeling is fundamental to synthetic biology, and this is why we have decided to characterise recombination as comprehensively as possible. To achieve this, we split it into four parts. First, we compare how the deterministic and stochastic models simulate our recombinase-expressing E. coli strains. Second, we use our stochastic model to predict recombination efficiency and apply it to help witht the model of the Technion Israel iGEM team. Third, we combine the above two models together to perform in silico experiments for our logic gates and pulse generators. Finally, we have built tools based on literature to estimate the number of off-target recombination sites inside a genome, as well as to predict the effect of distance between two target sites on the rate of recombination.
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With the ability to modify DNA in a precise manner, SSR has been used in various fields of research and industrial applications where genetic engineering is required.
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Revision as of 13:25, 18 October 2017
We are SMORE,
the University of Edinburgh's undergraduate 2017 iGEM team.
This summer, we are designing a site specific recombination toolkit for metabolic and genome engineering.
What is site-specific recombination and why does it matter?
We believe that mathematical modeling is fundamental to synthetic biology, and this is why we have decided to characterise recombination as comprehensively as possible. To achieve this, we split it into four parts. First, we compare how the deterministic and stochastic models simulate our recombinase-expressing E. coli strains. Second, we use our stochastic model to predict recombination efficiency and apply it to help witht the model of the Technion Israel iGEM team. Third, we combine the above two models together to perform in silico experiments for our logic gates and pulse generators. Finally, we have built tools based on literature to estimate the number of off-target recombination sites inside a genome, as well as to predict the effect of distance between two target sites on the rate of recombination.
Why are we creating a modular toolkit for SSR?
While researchers have benifitted enormously from the development of a few recombinases (Cre and FLP being the prime examples), we realise that the potential of SSR in genetic engineering is yet to be fully exploited. Researchers often have limited choices of recombinases for use in a particular chassis; and some have unexpected side effects. This puts restriction to the experimental design, hindering scientific discovery.
By developing a site-specific, modular recombination (SMORE) toolkit with each part thoroughly tested, we aim to contribute to the scientific community multiple usable recombination system, allowing the creation of complex gene circuits for synthetic biology and research applications.
What do we plan to include in the toolkit?
To test the utility of our toolkit we aim to design three proofs of concept: