Date of Award

August 2021

Document Type


Degree Name

Doctor of Philosophy (PhD)


Biochemistry and Molecular Biology

Committee Member

F. Alex Feltus

Committee Member

Trudy MacKay

Committee Member

Leigh Anne Clark

Committee Member

Miriam Konkel


Biological systems are incredibly difficult to untangle. On a molecular level, biological systems need to be analyzed by embracing high-dimensional genetic complexity in experimental design. One approach to understanding a biological system like a human organ (e.g., the normal brain) or disease state (e.g., brain tumor) is to identify condition-specific factors – biomarkers – that discriminate between biological states. A biomarker system is a group of biomarkers that are collectively associated with a phenotype. With exponentially growing amounts of high-throughput RNA-seq data available for both normal and different types of disease conditions, RNA-based biomarker systems, which underlie complex traits, can be identified. By using a combination of several computational biology approaches, including condition-specific gene co-expression networks (csGCNs) analysis, systems genetics integration of tissue-specific gene regulatory networks (tsGRNs), machine learning validation of biomarker systems, and dimensionality reduction techniques, robust RNA-based biomarker systems can be identified for both normal and disease states. In this Dissertation, I apply these methods to discover candidate biomarker systems involved in human normal brain region-specific states and normal lung versus lung tumor states. Chapter 1 provides overview of the field. Chapter 2 describes the identification of potential biomarker systems for normal human brain sub-regions by GCN network analysis. Chapter 3 shows the integration of csGCNs with lung-specific GRN to identify control-target biomarker systems for normal and cancerous lung tissue. Chapter 4 describes all other tissue-specific csGCNs constructed by KINC.



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