A disconnect exists between the behavior of transcriptional and genetic regulation in bacterial systems when comparing phenotypic patterns and dynamics of a population to those observed at the single cell level. In this thesis, we developed a number of tools and assays to better understand, overcome, and predict the challenges of heterogeneity within a population with the goal of enabling a better understanding of fundamental biology and the use of this understanding for engineering novel tools in E. coli. I describe our exploration of whether the strategy of conditional spatial sequestration of transcriptional regulators, a means by which bacteria naturally regulate native gene expression, could be engineered to create novel regulatory networks. We engineered an orthogonal conditional spatial sequestration system for transcriptional regulators which allows for the accumulation of said regulators within a cell that can be rendered functional in response treatment by a small molecule. I then describe our attempts to gain better understanding the regulation of the transfer machinery in the F plasmid, specifically why only a subset of cells within a donor population initiates conjugation. In particular, we examined the role of TraJ in regulating PY, the promoter associated with transcription regulation of most conjugative machinery. Through the development of two assays quantifying TraJ induction of PY and conjugative rates at a single cell level, we determined that while TraJ is required for induction from PY, expression of TraJ does not directly correlate to induction of PY, nor does the addition of orthogonal TraJ expression lead to an increase in conjugative rates. We then leveraged these observations to engineer| a novel orthogonally regulated conjugative transfer system. Finally, I describe our work on the creation of a model to aid in the analysis and of design of genetic circuits for genomic integration by predicting how distributions of genomic DNA evolve within bacterial cultures in relation to changes in growth. To inform this model, we generated a library of genomic distribution snapshots throughout an entire growth curve of multiple batch cultures to calibrate and validate a predictive, agent-based model capable of capturing variation in E. coli genome copy number across multiple phases of growth.

Last modified

• 02/20/2018

Creator

• Andrew Hilz Scarpelli

DOI

• https://doi.org/10.21985/N2NT12

Subject

• Interdepartmental Biological Sciences (IBiS) Graduate Program

Keyword

• Synthetic Biology
• Bacterial Signalling
• Genomic Regulation
• Conjugation
• Genomic Heterogeneity

Date created

• 2016-01-01

Resource type

• Dissertation

Rights statement

• In Copyright