Defense Date

2021

Document Type

Thesis

Degree Name

Master of Science

Department

Bioinformatics

First Advisor

Stephen Fong

Abstract

Measurements of gene expression find use in many areas related to the characterization of prokaryotes. For example, metabolic modeling is commonly employed to assist in the design of engineered microbes by helping to elucidate the organism’s extant metabolic processes. With insights generated from the modeling procedure, targeted genetic modifications can be introduced to the organism to bring about some desired effect. Metabolic models based upon the “flux balance analysis” method (or FBA) can be made more accurate by integrating gene expression data, such as that from RNAseq experiments. For various reasons, gene expression data is not always available for every organism nor is it always simple to obtain. Research has been undertaken to investigate the possibility of more efficiently utilizing the genomic data used to construct FBA metabolic models. It has previously been shown that portions of a given prokaryote’s genome can be used to predict gene expression levels with a certain degree of accuracy. This is accomplished by extracting DNA sequences believed to be regulatory in nature from target genomes, and fitting predictive models using that data in conjunction with annotated prokaryotic genomes. The predicted mRNA abundance could be integrated into a suitable FBA metabolic model to produce a more accurate metabolic model than a biomass function-based FBA model without any additional data collection.

Rights

© Jesse Raynor

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

8-13-2021

Available for download on Wednesday, August 12, 2026

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