Simultaneous modeling of concentration-effect and time-course patterns in gene expression data from microarrays.

BACKGROUND: Time-course and concentration-effect experiments with multiple time-points and drug concentrations provide far more valuable information than experiments with just two design-points (treated vs. control), as commonly performed in most microarray studies. Analysis of the data from such complex experiments, however, remains a challenge. MATERIALS AND METHODS: Here we present a semi-automated method for fitting time profiles and concentration-effect patterns, simultaneously, to gene expression data. The submodels for time-course included exponential increase and decrease models with parameters, such as initial expression level, maximum effect, and rate-constant (or half-time). The submodel for concentration-effect was a 4-parameter Hill model. RESULTS: The method was applied to an Affymetrix HG-U95Av2 dataset consisting of 51 arrays. The specific study focused on the effects of two platinum drugs, cisplatin and oxaliplatin, on A2780 human ovarian carcinoma cells. Replicates were available at most time points and concentrations. Eighteen genes were selected, and after selection, time-course and concentration-effect were modeled simultaneously. CONCLUSION: Comparisons of model parameters helped to distinguish genes with different expression patterns between the two drug treatments. This overall paradigm can help in understanding the molecular mechanisms of the agents, and the timing of their actions.