The use of semiparametric mixed models to analyze PamChip® peptide array data: an application to an oncology experiment

Abstract

Motivation: Phosphorylation by protein kinases is a central theme in biological systems. Aberrant protein kinase activity has been implicated in a variety of human diseases (e.g. cancer). Therefore, modulation of kinase activity represents an attractive therapeutic approach for the treatment of human illnesses. Thus, identification of signature peptides is crucial for protein kinase-targeting and can be achieved by using PamChipr microarray technology. we propose a flexible semi-parametric mixed model for analyzing PamChipr data. This approach enables the estimation of the phosphorylation rate (Velocity) as a function of time together with pointwise confidence intervals. Results: Using a publicly available dataset (Versele et al., 2009), we show that our model is capable of adequately fitting the kinase activity profiles and provides velocity estimates over time. Moreover, it allows to test for differences in the velocity of kinase inhibition between responding and non-responding cell lines. This can be done at individual time point as well as for the entire velocity profile.