Computational Approaches to Study Gene Regulatory Networks

Protocol
First Online:
Part of the Methods in Molecular Biology book series (MIMB, volume 1629)

Abstract

The goal of the gene regulatory network (GRN) inference is to determine the interactions between genes given heterogeneous data capturing spatiotemporal gene expression. Since transcription underlines all cellular processes, the inference of GRN is the first step in deciphering the determinants of the dynamics of biological systems. Here, we first describe the generic steps of the inference approaches that rely on similarity measures and group the similarity measures based on the computational methodology used. For each group of similarity measures, we not only review the existing approaches but also describe specifically the detailed steps of the existing state-of-the-art algorithms.

Key words

Gene regulatory networks Gene expression profiles Similarity measures Correlation Gaussian graphical models Information theory Regression Bayesian network 
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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Systems Biology and Mathematical Modeling GroupMax Planck Institute of Molecular Plant PhysiologyPotsdam-GolmGermany

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