Network biology has proven to be powerful tool for representing and analyzing complex molecular networks. It has also been successfully applied to biological field helping understand various biological processes. However, our current knowledge about the dynamics of gene networks during disease progression is rather limited. On the other hand, network construction is a prerequisite of network analysis. When the number of samples is limited, state-of-art computational methods for network construction are not robust in terms of low statistical power. In addition, molecular networks have been used extensively to improve the inference accuracy of causal coding variants, but this potential has not been investigated to the same extent for noncoding variants.
To address those limitations, I first developed inference of multiple differential modules (iMDM) algorithm to study network dynamics. This method is able to identify both unique and shared modules from multiple gene networks, each of which denoting a different perturbation condition. Using iMDM algorithm, I identified different types of modules to understand heart failure progression and disease dynamics.
Next, I developed a computational framework to construct condition specific transcriptional regulatory network. I also developed a computational method to rank transcription factors in the transcriptional regulatory network. Applying this framework to RNA-seq data for hematopoietic stem cell development, I successfully constructed corresponding transcriptional regulatory network and identified key transcriptional factors that play important roles.
Finally, I developed Annotation of Regulatory Variants using Integrated Networks (ARVIN), a network-based algorithm, to identify causal genetic variants for diseases. By applying ARVIN to various diseases, we obtained a systems understanding of the gene circuitry that is affected by all enhancer mutations in a given disease.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-6955 |
| Date | 01 May 2017 |
| Creators | Gao, Long |
| Contributors | Tan, Kai |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | dissertation |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright © 2017 Long Gao |
Page generated in 0.0119 seconds