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Connectivity in the yeast cell cycle transcription network : inferences from neural networks

Hart, Christopher E. and Mjolsness, Eric and Wold, Barbara J. (2006) Connectivity in the yeast cell cycle transcription network : inferences from neural networks. PLoS Computational Biology, 2 (12). pp. 1592-1607. ISSN 1553-734X. PMCID PMC1761652. http://resolver.caltech.edu/CaltechAUTHORS:HARploscb06

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Abstract

A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein: DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico “mutation” to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that “network-local discrimination” occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1371/journal.pcbi.0020169DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1761652/PubMed CentralArticle
ORCID:
AuthorORCID
Wold, Barbara J.0000-0003-3235-8130
Additional Information:© 2006 Hart et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: May 10, 2006; Accepted: October 30, 2006; Published: December 22, 2006 A previous version of this article appeared as an Early Online Release on October 30, 2006 (doi:10.1371/journal.pcbi.0020169.eor). Author contributions. CEH, EMJ, and BJW conceived and designed the experiments, analyzed the data, and wrote the paper. CEH implemented software and analytical methods required for work presented in the paper. Funding. The authors received no specific funding for this study. Competing interests. The authors have declared that no competing interests exist. Table of top regulators. Gene descriptions for the top ten positively and negatively associated regulators for each cluster as determined by the ANN weights matrix in Figure 4 with annotations from http://www.yeastgenome.org.
PubMed Central ID:PMC1761652
Record Number:CaltechAUTHORS:HARploscb06
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:HARploscb06
Alternative URL:http://dx.doi.org/10.1371/journal.pcbi.0020169
Official Citation:Hart CE, Mjolsness E, Wold BJ (2006) Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks. PLoS Comput Biol 2(12): e169 DOI: 10.1371/journal.pcbi.0020169
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:6805
Collection:CaltechAUTHORS
Deposited By: Archive Administrator
Deposited On:23 Dec 2006
Last Modified:02 May 2019 17:27

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