Expression data from ovarian cancer cells with time-course and concentration-profiles

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. 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.

51

Brun YF, Varma R, Hector SM, Pendyala L, Tummala R, Greco WR