Advisor Information
Kathryn Cooper
Location
UNO Criss Library, Room 231
Presentation Type
Oral Presentation
Start Date
3-3-2017 10:45 AM
End Date
3-3-2017 11:00 AM
Abstract
The use of high-throughput assays, or experiments yielding large data sets, in biological research has become a standard practice in laboratories throughout the world. Because such investigations have the ability to produce high volume and comprehensive data sets, it is then important to develop methods that allow researchers to quickly pull meaningful information from an overwhelming amount of data. Network modeling has become a popular technique for visualizing and analyzing large biological data sets. A network is a basic graph with nodes and edges (i.e. social networks) that also integrates complex principles of graph theory for deeper analysis and pattern discovery. In biological research, networks have successfully modeled protein interactions within a cell, gene expression rates, and the correlation or relationships between gene expression when zooming into a specific biological pathway or process. In my research, I have collected gene expression data for network modeling and pattern discovery in the hope of identifying key genes involved in the shift that yeast undergo from active proliferation and development to a state of dormancy. Using a method called gateway node analysis, I am aiming to analyze gene expression networks for dense regions, or clusters, which may represent genes under similar gene expression regulation. Then, gateway node analysis will allow me to predict specific genes that may be responsible or important for this shift. This analysis technique, once further validated, could serve to predict genes involved in many biological pathways for disease research and other medical applications.
Identification of genes involved in diauxic shift of saccharomyces cerevisiae through gateway node analysis.
UNO Criss Library, Room 231
The use of high-throughput assays, or experiments yielding large data sets, in biological research has become a standard practice in laboratories throughout the world. Because such investigations have the ability to produce high volume and comprehensive data sets, it is then important to develop methods that allow researchers to quickly pull meaningful information from an overwhelming amount of data. Network modeling has become a popular technique for visualizing and analyzing large biological data sets. A network is a basic graph with nodes and edges (i.e. social networks) that also integrates complex principles of graph theory for deeper analysis and pattern discovery. In biological research, networks have successfully modeled protein interactions within a cell, gene expression rates, and the correlation or relationships between gene expression when zooming into a specific biological pathway or process. In my research, I have collected gene expression data for network modeling and pattern discovery in the hope of identifying key genes involved in the shift that yeast undergo from active proliferation and development to a state of dormancy. Using a method called gateway node analysis, I am aiming to analyze gene expression networks for dense regions, or clusters, which may represent genes under similar gene expression regulation. Then, gateway node analysis will allow me to predict specific genes that may be responsible or important for this shift. This analysis technique, once further validated, could serve to predict genes involved in many biological pathways for disease research and other medical applications.