Author ORCID Identifier

0000-0001-6278-1074

Date of Award

5-1-2023

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Alex Zelikovsky

Second Advisor

Pavel Skums

Third Advisor

Murray Patterson

Fourth Advisor

Artem Rogovskyy

Abstract

Metabolic pathways are a series of enzyme-mediated reactions that result in the transformation of substances from one form to another. While methods for studying metabolic pathways are constantly improving, analyzing these pathways can be challenging. To accurately predict metabolic pathway activity, it is essential to understand and quantify the relative involvement of enzymes in these pathways. In my dissertation, I propose a novel method based on the maximum likelihood Expectation-Maximization (EM) algorithm to estimate metabolic pathway activity levels using enzyme participation as a latent variable. This improved maximum likelihood model will be used to conduct downstream analysis of metabolic pathway expression, which will be estimated from RNA-Seq samples obtained from rodents and a planktonic microbial community.

DOI

https://doi.org/10.57709/35365476

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