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Computational Approaches to Study Gene Regulatory Networks.

Nooshin Omranian1, Zoran Nikoloski2

  • 1Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm, 14476, Germany.

Methods in Molecular Biology (Clifton, N.J.)
|June 18, 2017
PubMed
Summary
This summary is machine-generated.

Inferring gene regulatory networks (GRNs) reveals gene interactions from spatiotemporal expression data. Understanding GRNs is crucial for deciphering biological system dynamics and cellular processes.

Keywords:
Bayesian networkCorrelationGaussian graphical modelsGene expression profilesGene regulatory networksInformation theoryRegressionSimilarity measures

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Gene regulatory networks (GRNs) govern cellular processes by controlling gene expression.
  • Inferring GRNs is essential for understanding the dynamics of biological systems.
  • Spatiotemporal gene expression data provides insights into gene interactions.

Purpose of the Study:

  • To outline the general steps in gene regulatory network inference.
  • To categorize similarity measures used in GRN inference based on computational methods.
  • To review existing GRN inference approaches and detail state-of-the-art algorithms.

Main Methods:

  • Review of GRN inference methodologies.
  • Classification of similarity measures by computational approach.
  • Detailed description of state-of-the-art GRN inference algorithms.

Main Results:

  • A structured overview of GRN inference approaches.
  • Categorization of similarity measures based on computational techniques.
  • In-depth explanation of current leading GRN inference algorithms.

Conclusions:

  • The study provides a comprehensive review of GRN inference techniques.
  • It offers a systematic approach to understanding similarity measures in GRN inference.
  • This work facilitates the deciphering of gene interactions and biological system dynamics.