Novel High-Throughput Methods for Rapid Development of Cell Factories

Michael Schantz Klausen

Research output: Book/ReportPh.D. thesis

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The introduction of high-throughput sequencing of nucleotide sequences has sparked a revolution in biology. Reading nucleotide sequences was once an expensive and laborious process has since evolved into a rapid and efficient process with applications beyond simply reading the primary nucleotide composition of a given genome. A wealth of different techniques exist to encode information about cellular processes into nucleic acid sequences which can be read and recorded by
sequencing instruments. As the capacity of these instruments continue to grow, so does our ability to efficiently and rapidly probe mechanisms in microbial life forms. Microbial cell factories are a promising solution to a number of problems related to the sustainability of our modern society. Using microbial life forms to produce chemical compounds takes advantage of the diversity of efficient mechanisms evolved over billions of years. Engineering microbial cell factories are still a pursuit fraught with obstacles, due to the inherent complexity of living organisms. In this thesis several new tools are presented with the main goal of accelerating engineering of new microbial cell factories through the use of high-throughput technology. A massively parallel reporter assay is developed to record expression levels of genetic elements of microbes living in the gut of a mouse model to assist in the engineering of advanced cell factories producing therapeutics in situ. The assay is used to deeply characterize the main housekeeping promoter of Escherichia coli. Furthermore, the feasibility of using nanopore sequencing technology to
read non-standard nucleotides in ribonucleic acid (RNA). It was found possible to read several native modifications to ribosomal RNA in E. coli, with further studies pending as to whether a methodology can be developed to probe chemically induced modifications. Finally, a deep neural network model was developed to predict structural features of proteins from the primary amino acid sequence.
Original languageEnglish
Place of PublicationKgs. Lyngby
PublisherTechnical University of Denmark
Number of pages137
Publication statusPublished - 2019


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