Inferring biological networks from genome-wide transcriptional and fitness data

Varsally, Wazeer Mohammad (2014). Inferring biological networks from genome-wide transcriptional and fitness data. University of Birmingham. Ph.D.

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Abstract

In the last 15 years, the increased use of high throughput biology techniques such as genome-wide gene expression profiling, fitness profiling and protein interactomics has led to the generation of an extraordinary amount of data. The abundance of such diverse data has proven to be an essential foundation for understanding the complexities of molecular mechanisms and underlying pathways within a biological system.
This thesis demonstrates the capabilities and applications of using biological networks to extrapolate biological information from the wealth of data available in the yeast species Saccharomyces cerevisiae and Schizosaccharomyces pombe. This study marks the first time a mutual information based network inference approach has been applied to a set of specific genome-wide expression and fitness compendia.
In particular, this work has generated hypotheses in S. pombe that have led to a deeper understanding of the relationship between ribosomal proteins and energy metabolism, a recently discovered pathway termed riboneogenesis. Experimental validation of this hypothesis has led to new theories on the role of energy metabolism enzymes in controlling ribosome biogenesis in S. pombe, including the novel finding that fructose-1, 6-bisphosphatase (FBP1) may have roles in both gluconeogenesis and riboneogenesis.
This thesis also demonstrates how the use of multi-level data allows for comprehensive insight into nuclear functions of the S. pombe nonsense-mediated mRNA decay protein, UPF1. This study provides substantial evidence demonstrating the role of UPF1 in DNA replication. The applicability of fitness data in identifying targets of metal and metalloid toxicity in S. cerevisiae has also been investigated.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):