Computational Systems Biology Methods and Protocols.7z

Chapter 8

The Reconstruction and Analysis of Gene Regulatory

Networks

Guangyong Zheng and Tao Huang

Abstract

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e.,
gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene
regulatory networks (GRNs) are constructed to show relationship between biological molecules, in
which the vertices of network denote biological molecules and the edges of network present connection
between nodes (Strogatz, Nature 410:268–276, 2001; Bray, Science 301:1864–1865, 2003). Biologists
can understand not only the function of biological molecules but also the organization of components of
living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physio-
logical map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature
410:268–276, 2001; Bray, Science 301:1864–1865, 2003). In this paper, we will review the inference
methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for
studying complex diseases and biological processes, the applications of the network method in pathway
analysis and disease gene identification will be introduced.

KeywordsGene regulatory network, Network reconstruction, Module detection, Pathway analysis,

Disease gene identification

1 Introduction

In general, a gene regulatory network is established with a reverse

engineering strategy, in which gene expression data is utilized as

input and topology structure of network is generated as output

[2, 3]. With the development of high-throughput technology, gene

expression data is accumulated with an unprecedented speed and

thus provides sufficient source data for GRN reconstruction. Now-

adays, gene expression data can be collected from public database,

for example, the Gene Expression Omnibus (GEO) database (www.

ncbi.nlm.nih.gov/geo), a comprehensive microarray data reposi-

tory [4]; the Sequence Read Archive (SRA) database (www.ncbi.

nlm.nih.gov/sra), a data warehouse storing next-generation

sequencing data [5]; and the Expression Atlas (www.ebi.ac.uk/

Tao Huang (ed.),Computational Systems Biology: Methods and Protocols, Methods in Molecular Biology, vol. 1754,
https://doi.org/10.1007/978-1-4939-7717-8_8,©Springer Science+Business Media, LLC, part of Springer Nature 2018

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