The complex molecular machinery of the cell controls its response to various signals and environmental conditions. A natural approach to study these molecular mechanisms and cellular processes is with protein interaction networks. Due to the complexity of these networks,
sophisticated computational techniques are required to extract biological insights from them.
In this thesis, I develop and apply network-based algorithms for three different challenges.
1. I develop a novel, highly-scalable algorithm for network-based label prediction methods
that enables the integration of functional annotations and interaction networks across
many species in order to predict the functions of genes in newly-sequenced bacteria.
2. To overcome the limitations of experimental approaches to find human proteins and
processes that are hijacked by SARS-CoV-2, I adapt network propagation approaches
for predicting human interactors of the virus.
3. Large-scale experimental techniques to screen chemicals for toxicity have tested their
effects on many individual proteins. I integrate human protein-protein interactions with
this data to gain insights into the molecular networks those chemicals affect.
For each of these research problems, I perform comprehensive evaluations and downstream
analyses to demonstrate both the accuracy of our approaches and their utility in obtaining a
broader understanding of the molecular systems in question. / Doctor of Philosophy / The functions of all living cells are governed by complex networks of molecular interactions.
A major goal of systems biology is to understand the components of this machinery and
how they regulate each other to control the cell's response to various conditions and signals.
Advances in experimental techniques to understand these systems over the past couple
of decades have led to an explosion of data that probe various aspects of a cell such as
genome sequencing, which reads the DNA blueprint, gene expression, which measures the
amount of each gene's products in the cell, and the interactions between those products
(i.e., proteins). To extract biological insights from these datasets, increasingly sophisticated
computational methods are required. A powerful approach is to model the datasets as networks where the individual molecules are the nodes and the interactions between them are
the edges. In this thesis, I develop and apply network-based algorithms to utilize molecular
systems data for three related problems: (i) predicting the functions of genes in bacterial
species, (ii) predicting human proteins and processes that are hijacked by the SARS-CoV-2
virus, and (iii) suggesting cellular signaling pathways affected by exposure to a chemical.
Developments such as those presented in these three projects are critical to obtaining a
broader understanding of the functions of genes in the cell. Therefore, I make the methods
and results for each project easily accessible to aid other researchers in their efforts.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/110406 |
| Date | 09 December 2020 |
| Creators | Law, Jeffrey Norman |
| Contributors | Genetics, Bioinformatics, and Computational Biology, Murali, T. M., Rajagopalan, Padmavathy, Heath, Lenwood S., Kale, Shiv Dutt |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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