The Phenotype MicroArray technology of Biolog, Inc. (Hayward, CA) measures the respiration of cells as a function of time in thousands of microwells simultaneously, and thus provides a high-throughput means of studying cellular phenotypes. The microwells contain compounds involved in a number of biochemical pathways, as well as chemicals that test the sensitivity of cells against antibiotics and stress. While the PM experimental workflow is completely automated, statistical methods to analyze and interpret the data are lagging behind. To take full advantage of the technology, it is essential to develop efficient analytical methods to quantify the information in the complex datasets resulting from PM experiments. We propose the use of statistical growth-curve models to rigorously quantify observed differences in PM experiments, in the context of the growth and metabolism of Yersinia pestis cells grown under different physiological conditions. The information from PM experiments complement genomic and proteomic results and can be used to identify gene function and in drug development. Successful coupling of phenomics results with genomics and proteomics will lead to an unprecedented ability to characterize bacterial function at a systems biology level
