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An elastic network model to identify characteristic stress response genes

Paper ID Volume ID Publish Year Pages File Format Full-Text
15219 1392 2010 10 PDF Available
Title
An elastic network model to identify characteristic stress response genes
Abstract

Exposing eukaryotic cells to a toxic compound and subsequent gene expression profiling may allow the prediction of selected toxic effects based on changes in gene expression. This objective is complicated by the observation that compounds with different modes of toxicity cause similar changes in gene expression and that a global stress response affects many genes. We developed an elastic network model of global stress response with nodes representing genes which are connected by edges of graded coexpression. The expression of only few genes have to be known to model the global stress response of all but a few atypical responder genes. Those required genes and the atypical response genes are shown to be good biomarker for tox predictions. In total, 138 experiments and 13 different compounds were used to train models for different toxicity classes. The deduced biomarkers were shown to be biologically plausible. A neural network was trained to predict the toxic effects of compounds from profiling experiments. On a validation data set of 189 experiments with 16 different compounds the accuracy of the predictions was assessed: 14 out of 16 compounds have been classified correctly. Derivation of model based biomarkers through the elastic network approach can naturally be extended to other areas beyond toxicology since subtle signals against a broad response background are common in biological studies.

Keywords
Gene expression; Microarray data; Toxicology prediction; Stress response; Pharmacology; Drug development
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 34, Issue 3, June 2010, Pages 193–202
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us