Exploring the operational characteristics of inference algorithms for transcriptional networks by means of synthetic data.

The development of structure-learning algorithms for gene regulatory networks depends heavily on the availability of synthetic data sets that contain both the original network and associated expression data. This article reports the application of SynTReN, an existing network generator that samples topologies from existing biological networks and uses Michaelis-Menten and Hill enzyme kinetics to simulate gene interactions. We illustrate the effects of different aspects of the expression data on the quality of the inferred network.

Molecular responses during cadmium-induced stress in Daphnia magna: integration of differential gene expression with higher-level effects.

DNA microarrays offer great potential in revealing insight into mechanistic toxicity of contaminants. The aim of the present study was (i) to gain insight in concentration- and time-dependent cadmium-induced molecular responses by using a customized Daphnia magna microarray, and (ii) to compare the gene expression profiles with effects at higher levels of biological organization (e.g.

Validating module network learning algorithms using simulated data.

Background: In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms.

Effluent impact assessment using microarray-based analysis in common carp: a systems toxicology approach.

Effluents are a main source of direct and continuous input of pollutants to the aquatic environment, and can cause ecotoxicological effects at different levels of biological organization. Since gene expression responses represent the primary interaction site between environmental contaminants and biota, they provide essential clues to understand how chemical exposure can affect organismal health. The aim of the present study was to investigate the applicability of a microarray approach for unraveling modes of action of whole effluent toxicity and impact assessment.

Daphnia magna and ecotoxicogenomics: gene expression profiles of the anti-ecdysteroidal fungicide fenarimol using energy-, molting- and life stage-related cDNA libraries.

In the present study, the existing life stage-specific cDNA library was extended with energy- and molting-related genes using Suppression Subtractive Hybridization PCR and a microarray for the aquatic test organism Daphnia magna was created. A gene set of 2455 fragments was produced belonging to different pathways such as carbohydrate and lipid metabolism, O2 transport and heme metabolism, immune response, embryo development, cuticula metabolism and visual perception pathways. Using this custom microarray, gene expression profiles were generated from neonates exposed to three concentrations of the anti-ecdysteroidal fungicide fenarimol (0.

Patterns of gene expression in carp liver after exposure to a mixture of waterborne and dietary cadmium using a custom-made microarray.

Gene expression changes in carp liver tissue were studied after acute (3 and 24h) and subchronic (7 and 28 days) exposure to a mixture of waterborne (9, 105 and 480 microg/l) and dietary (9.5, 122 and 144 microg/g) cadmium, using a custom-made microarray. Suppression subtractive hybridization-PCR (SSH-PCR) was applied to isolate a set of 643 liver genes, involved in multiple biological pathways, such as energy metabolism (e.

Neuropharmaceuticals in the environment: mianserin-induced neuroendocrine disruption in zebrafish (Danio rerio) using cDNA microarrays.

Because of their environmental occurrence and high biological activity, human pharmaceuticals have received increasing attention from environmental and health agencies. A major bottleneck in their risk assessment is the lack of relevant and specific effect data. We developed an approach using gene expression analysis in quantifying adverse effects of neuroendocrine pharmaceuticals in the environment.

Expression profiling of endocrine-disrupting compounds using a customized Cyprinus carpio cDNA microarray.

Exposure to a variety of anthropogenic compounds has been shown to interfere with normal development, physiology, and reproduction in a wide range of organisms, both in laboratory studies and wildlife. We have developed a Cyprinus carpio cDNA microarray consisting of endocrine-related genes. In the current study, we investigated the applicability of this microarray (1) to study the molecular effects induced by exposure to a variety of endocrine-disrupting compounds (EDCs) in fish and (2) to discriminate the specific transcriptional profiles associated with these compounds.

Effects of the antidepressant mianserin in zebrafish: molecular markers of endocrine disruption.

Due to their environmental occurrence and intrinsic biological activity, human pharmaceuticals have received increasing attention from environmental and health agencies. Of particular, ecotoxicological concern are drugs that affect nervous- and endocrine-systems. Zebrafish genome-wide oligo arrays are used to collect mechanistic information on mianserin-induced changes in gene expression in zebrafish.

SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.

Background: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner.