How to infer gene networks from expression profiles

Inferring, or ‘reverse‐engineering’, gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse‐engineering algorithm...

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Bibliographic Details
Published in:Molecular systems biology 2007, Vol.3 (1), p.78-n/a
Main Authors: Bansal, Mukesh, di Bernardo, Diego, Belcastro, Vincenzo, Ambesi-Impiombato, Alberto
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Computational Biology
Computer applications
DNA microarrays
Engineering
Experimental data
Experiments
Gene expression
Gene Expression Profiling - methods
Gene Expression Regulation
gene network
gene regulation
Gene Regulatory Networks
Genes
Oligonucleotide Array Sequence Analysis
Perturbation
Proteins
reverse-engineering
Review
Software
Systems Biology - methods
transcriptional regulation
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Summary:Inferring, or ‘reverse‐engineering’, gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse‐engineering algorithms for which ready‐to‐use software was available and that had been tested on experimental data sets. We show that reverse‐engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.
ISSN:1744-4292
1744-4292
DOI:10.1038/msb4100120