1 Algorithms for E . coli Genome Engineering

1. Abstract Genome engineering refers to the tools and techniques developed to perform targeted editing of a genome. This project focuses specifically on modifying regions in the E. coli genome by integrating DNA constructs. In order to prevent the disruption of the host genome, these regions, or integration sites, must be carefully chosen such that the insertion of DNA constructs is specific, i.e. the construct is only inserted at the intended site. We developed a way to perform large-scale integrations and build a dataset that allowed us to investigate the correlations between the characteristics of regions and their associated specificities. Using computational tools, we characterized the specificity of 150 genomic sites in E. coli. Additionally we discovered Repetitive Extragenic Palindrome (REP) sites that have high levels of efficiency but varying levels of specificity. We suggest that these sites could potentially be useful regions to target, depending on the application. Acknowledgements: I would like to thank my supervisor Dr. Oliver Purcell for always teaching me patiently and mentoring me about issues related to science and beyond. Without his guidance, support and humor, this project would not have been possible. I would also like to thank Professor Timothy Lu for always being supportive and providing me with all the resources necessary for conducting my research. I dedicate my work to my family for their unconditional love and support, always. Lastly, thank you Aziz, Eta, Lina & Noor – getting through MIT without your support would not have been possible. Lamba red recombineering is one of methods of performing genome engineering. However, this method of genome editing is not very specific and efficient and is highly dependent on the genomic regions that are targeted (integration sites). In this project we explored ways of identifying what makes a site well suited for lambda red genome engineering. We wanted to explore whether we can eventually predict the " goodness " of an integration site using an algorithm. Our initial approach to the problem was to write an algorithm based on some characteristics that we felt would be key to determining the goodness of a site. Choosing to initially focus on specificity of the integrations, we used experimental approaches to evaluate whether our algorithm had any predictive powers for specificity. Upon failing, we revised our plan to generate a dataset of ~150 sites and their integration data (whether integration was successful, specific and efficient at that …