As a biological organism encounters various experimental conditions (such as environmental stress, a mutation or exposure to a toxin), a plethora of responses are initiated within the cells of the organism. These responses occur over time as each cell exposed to a new condition attempts to accommodate or adjust. The responses within each cell occur at multiple levels including gene expression, protein abundance and metabolite levels. Measurements of these changing levels within the cell result in large amounts of high-dimensional time-course data. Due to the amount and complexity of the data, it is important to have good tools for identifying general patterns within each high dimensional time-course, and key differences across related time courses. Knowing how gene expression or protein abundance levels are affected by a particular condition is important because it allows us to gain insight into the workings of cells and/or whole organisms. In our research, we are developing and improving methods for investigating high dimensional time-course data. Although there are several types of data that our methods can be applied to interrogate, we are currently focusing on gene expression data. An example of the type of data we work with is a toxicology dataset collected in the Bradfield lab here at UW-Madison. This time-course gene expression data is collected from mouse livers cells under several conditions. Each condition consists of exposure to one toxin. The exception is the wildtype condition which is not exposed to a toxin and serves as a control. We are applying our methods to this dataset in order to identify similarities and differences among the responses induced by different toxins.