학술논문
Statistical Approaches to Genome-wide Biological Networks
Document Type
Article
Author
Source
(2009): 190-202.
Subject
Language
Korean
ISSN
19760280
Abstract
The experiments based on high-throughput technology have been producing massive genomic data including protein-protein interactions, genome-wide mRNA expression and whole genome sequences, which allows the reconstruction of genome-wide biological networks representing relationships or interactions between genes or proteins. The network approach to biology is becoming the main framework to understand biological systems consisting of numerous dynamic networks of biochemical reactions and signaling interactions between cellular components. This is mainly due to efficient representation of a large amount of biological information. Many statistical models have been built and applied to construction of genome-wide biological networks from various type of high-throughput data. In this study, we survey statistical approaches to construction of four main biological networks with their pros and cons: gene regulatory networks, protein-protein interaction networks, metabolic networks and signal transduction networks. In addition, we also investigate the methods describing dynamic behavior of gene regulatory networks and signal transduction networks.
The experiments based on high-throughput technology have been producing massive genomic data including protein-protein interactions, genome-wide mRNA expression and whole genome sequences, which allows the reconstruction of genome-wide biological networks representing relationships or interactions between genes or proteins. The network approach to biology is becoming the main framework to understand biological systems consisting of numerous dynamic networks of biochemical reactions and signaling interactions between cellular components. This is mainly due to efficient representation of a large amount of biological information. Many statistical models have been built and applied to construction of genome-wide biological networks from various type of high-throughput data. In this study, we survey statistical approaches to construction of four main biological networks with their pros and cons: gene regulatory networks, protein-protein interaction networks, metabolic networks and signal transduction networks. In addition, we also investigate the methods describing dynamic behavior of gene regulatory networks and signal transduction networks.
The experiments based on high-throughput technology have been producing massive genomic data including protein-protein interactions, genome-wide mRNA expression and whole genome sequences, which allows the reconstruction of genome-wide biological networks representing relationships or interactions between genes or proteins. The network approach to biology is becoming the main framework to understand biological systems consisting of numerous dynamic networks of biochemical reactions and signaling interactions between cellular components. This is mainly due to efficient representation of a large amount of biological information. Many statistical models have been built and applied to construction of genome-wide biological networks from various type of high-throughput data. In this study, we survey statistical approaches to construction of four main biological networks with their pros and cons: gene regulatory networks, protein-protein interaction networks, metabolic networks and signal transduction networks. In addition, we also investigate the methods describing dynamic behavior of gene regulatory networks and signal transduction networks.