Date of Award
Winter 12-15-2021
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Cell-to-cell variation in gene expression and metabolite levels have a significant impact on ensemble productivity of microbial bioproduction. New metabolic engineering tools and approaches are needed that consider cell cultures as an amalgam of uniquely behaving individuals to improve the slow commercialization of metabolically engineered systems. Stochastic cellular process including gene expression, metabolism, and growth lead to phenotypic variation between genetically identical cells. Understanding and the ability to control microbial phenotypic variation is key to improving microbial bioproduction metrics. During protein translation, codon usage strongly influences ensemble gene expression but the effect on the variation of gene expression was not well understood. Metabolite heterogeneity and dynamics are challenging to characterize due to the difficulties in measuring single cell metabolite levels. Advances in single cell analysis, including high-throughput sequencing and microfluidics-based microscopy, have enabled quantification of both protein and metabolite concentrations in single cells, revealing previous unknown phenomenon. Additionally, mathematical modeling of single cells provides a powerful tool for rapidly characterizing metabolic engineering strategies. This dissertation project aimed to use single cell analysis tools and modeling to understand 1) the effect of codon usage on gene expression variation and 2) the dynamics of protein and metabolite production in single cells and their influence to bioproduction from the cell population.
Language
English (en)
Chair
Fuzhong Zhang
Committee Members
Barak Cohen
Comments
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