Inferring cellular networks – a review

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Abstract

In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.

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Most cited references 136

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Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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A gene-coexpression network for global discovery of conserved genetic modules.

Joshua M. Stuart, Eran Segal, Daphne Koller … (2003)

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.

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Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.

Srinivasan Iyer, Michael Eisen, A Futcher … (1998)

We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: alpha factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle-regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu

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Author and article information

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date Collection: 2007

Publication date (Electronic): 27 September 2007

Volume: 8

Issue: Suppl 6

Page: S5

Affiliations

[1 ]Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany

[2 ]Princeton University, Lewis-Sigler Institute for Integrative Genomics and Dept. of Computer Science, Princeton, NJ 08544, USA

[3 ]Present affiliation: University Regensburg, Institute of Functional Genomics, Josef-Engert-Str. 9, 93053 Regensburg, Germany

Article

Publisher ID: 1471-2105-8-S6-S5

DOI: 10.1186/1471-2105-8-S6-S5

PMC ID: 1995541

PubMed ID: 17903286

SO-VID: fcb84091-4ff2-4e80-a4fe-049c75a9f1be

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Inferring cellular networks – a review

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Abstract

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Microbial Genomics

Most cited references 136

Gene Ontology: tool for the unification of biology

A gene-coexpression network for global discovery of conserved genetic modules.

Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.

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