A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.

The design, construction, operation, and maintenance of the Replicated Modular Estuarine Mesocosm

Perhaps the most difficult job of the ecotoxicologist is extrapolating data calculated from laboratory experiments with high precision and accuracy into the real world of highly-dynamics aquatic environments. The establishment of baseline laboratory toxicity testing data for individual compounds and ecologically important and field studies serve as a precursor to ecosystem level studies needed for ecological risk assessment. The first stage in the field portion of risk assessment is the determination of actual environmental concentrations of the contaminant being studied and matching those concentrations with laboratory toxicity tests. Risk estimates can be produced via risk quotients that would determine the probability that adverse effects may occur. In this first stage of risk assessment, environmental realism is often not achieved. This is due, in part, to the fact that single-species laboratory toxicity tests, while highly controlled, do not account for the complex interactions (Chemical, physical, and biological) that take place in the natural environment. By controlling as many variables in the laboratory as possible, an experiment can be produced in such a fashion that real effects from a compound can be determined for a particular test organism. This type of approach obviously makes comparison with real world data most difficult. Conversely, field oriented studies fall short in the interpretation of ecological risk assessment because of low statistical power, lack of adequate replicaiton, and the enormous amount of time and money needed to perform such studies. Unlike a controlled laboratory bioassay, many other stressors other than the chemical compound in question affect organisms in the environment. These stressors range from natural occurrences (such as changes in temperature, salinity, and community interactions) to other confounding anthropogenic inputs. Therefore, an improved aquatic toxicity test that will enhance environmental realism and increase the accuracy of future ecotoxicological risk assessments is needed.