Abstract
AbstractIn the past few years, many high-throughput techniques have been developed and applied to biological studies. These techniques such as “next generation” genome sequencing, chip-on-chip, microarray and so on can be used to measure gene expression and gene regulatory elements in a genome-wide scale. Moreover, as these technologies become more affordable and accessible, they have become a driving force in modern biology. As a result, huge amount biological data have been produced, with the expectation of increasing number of such datasets to be generated in the future. High-throughput data are more comprehensive and unbiased, but ‘real signals’ or biological insights, molecular mechanisms and biological principles are buried in the flood of data. In current biological studies, the bottleneck is no longer a lack of data, but the lack of ingenuity and computational means to extract biological insights and principles by integrating knowledge and high-throughput data. Here I am reviewing the concepts and principles of network biology and the computational methods which can be applied to cancer research. Furthermore, I am providing a practical guide for computational analysis of cancer gene networks.
Highlights
We can ask inspiring and fundamental questions and develop elegant computational methods using the principles of networks and statistics, which lead to new biological insights from high-throughput data
Several types of cellular networks have been found in cells: protein interaction networks, metabolic networks, gene regulatory networks and signaling networks, genetic interaction network and gene co-expression network (Wang et al 2007), which can be further classified into two categories based on whether the networks capture biological relations in genome-wide or a specific cellular condition
Positive feedback loops lean to emergent network properties such as ultrasensitivity, bistability and switch-like behavior, while negative feedback loops perform adaptation, desensitization, and preservation of homeostasis (Ferrell 2002). These motifs are enriched with the transcription factors whose mRNAs have fast decay rates, suggesting that motif structures encode a regulatory behavior: they are able to rapidly respond to internal and external stimuli and decrease cell internal noise (Wang et al 2005)
Summary
In the past few years, many high-throughput techniques have been developed and applied to biological studies These techniques such as “ generation” genome sequencing, chip-on-chip, microarray and so on can be used to measure gene expression and gene regulatory elements in a genome-wide scale. With the huge amount of data produced by highthroughput techniques, biologists have to deal thousands of biological relations in a single experiment In this situation, the traditionally descriptive ways for biological relations are not sufficient to deal with the huge number of relations under study. Network biology involves the use of networks to represent complexity, computes biological relationships and seeks to uncover biological principles and insights. We can ask inspiring and fundamental questions and develop elegant computational methods using the principles of networks and statistics, which lead to new biological insights from high-throughput data. Insightful results from these analyses can be used to ask new questions and design wet lab experiments
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