Practical Approaches to Biological Network Discovery

This dissertation addresses a current outstanding problem in the field of systems biology, which is to identify the structure of a transcriptional network from high-throughput experimental data. Understanding of the connectivity of a transcriptional network is an important piece of the puzzle, which relates the genotype of an organism to its phenotypes. An overwhelming number of computational approaches have been proposed to perform integrative analyses on large collections of high-throughput gene expression datasets to infer the structure of transcriptional networks. I put forth a methodology by which these tools can be evaluated and compared against one another to better understand their strengths and weaknesses. Next I undertake the task of utilizing high-throughput datasets to learn new and interesting network biology in the pathogenic fungus Cryptococcus neoformans. Finally I propose a novel computational method for mapping out transcriptional networks that unifies two orthogonal strategies for network inference. I apply this method to map out the transcriptional network of Saccharomyces cerevisiae and demonstrate how network inference results can complement chromatin immunoprecipitation: ChIP) experiments, which directly probe the binding events of transcriptional regulators. Collectively, my contributions improve both the accessibility and practicality of network inference methods.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science and Engineering

Year dc:date.available

2012

Author dc:creator

• Haynes, Brian

Contributors dc:contributor

• Michael Brent

• Computer science
• Bioinformatics
• systems biology
• transcriptional networks

Language dc:language

English (en)

DOI

https://doi.org/10.7936/K72805PN

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:etd-1695