Graph-Based Clustering Approaches for Gene Network Reconstruction
Date Issued
2009
Date
2009
Author(s)
Lai, Jhih-Siang
Abstract
To understand regulatory relationships between genes in real life. Biologists often use RNA interference (RNAi) or knockout genes to observe the response in the real life system. Informationists try to reconstruct regulatory relationship between genes from mRNA expression profile by algorithms or mathematic models. There are several phases involved in gene regulation such as transcription, post-transcriptional modifications, translation, RNA degradation and post-translational modifications .Time is essential for all these phases to be completed and many researches analyze regulation via these features. n this study, we use two methods to reconstruct regulatory relationships between genes. One is a graph partition algorithm named Normalized Cuts for partitioning off genes into functional gene network. The other method, PARE (Pattern Recognition Approach), an algorithm based on time-lagged non-linear feature of the profile, is to infer regulation between genes. In addition, we use yeast microarray to construct gene regulatory networks and check results from KEGG pathway database, BIOGRID interaction database and MIPS database. Comparing our F score result with Dynamic Bayesian Network developed by Kim, et al., it shows that our method performs better than theirs. inally, we apply our method to a real case in yeast microarray in which yox1 and yhp1 are both deleted and we analyze its mRNA expression time profile. Although mechanisms between phases in cell cycle are not clear, yox1 and yhp1 are two genes known controlling duration of a cell in G1 phase by negative feedback. We successfully find networks associated with cell cycle and one of the networks is associated with cell mitosis. In the future, we hope to decipher more mechanisms between phases in cell cycle.
Subjects
Gene Network
Normalized Cuts
Time Lag
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-98-R95548057-1.pdf
Size
23.53 KB
Format
Adobe PDF
Checksum
(MD5):e5ed41656a8ff1c719d3396ec3ff8455