Discovering Significant Pathways of Gene Regulation
Mentor: Serdar Bozdag
Summary: Recent advancements in biotechnology have made it possible to generate vast amounts of gene expression data for thousands of organisms. The collection of high-throughput gene expression data allows computational biologists to develop algorithms to reverse engineer the underlying gene regulatory network (GRN) of cells from their gene expression data. Among several software tools to reverse engineer GRNs, FastMEDUSA is a powerful tool. FastMEDUSA builds a model represented by an alternating decision tree (ADT) that predicts the potential regulators of genes.
We hypothesize that if there are significantly overrepresented branches in the ADT of FastMEDUSA, they could be biologically important pathways for gene regulation. In this project, we analyze the ADT built by FastMEDUSA to compute significantly overrepresented branches. We compute p-value for each branch based on results on randomly generated ADTs. For validation, we check public databases and literature to verify if genes in these branches have been reported as pathways of gene regulation.
Students will have the opportunity to work on graph theory, statistics, and biological databases to answer some high-impact biological questions.
1) Using an already existing computational model for predicting gene expression based on the presence of microRNA and other transcription factors, contribute to a list of known gene regulators and co-regulators.
2) Create an R package from the current gene expression prediction model that can be downloaded as a library.
Week 1: Learn R (already complete!)
Week 2: Become familiar with general molecular biology concepts and how the computational model works.
Week 3: Start running the model on sample data.