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Published on: January 22, 2013
The use of semiparametric mixed models to analyze PamChip(R) peptide array data: an application to an oncology
Pushpike J Thilakarathne1, Lieven Clement, Dan Lin
1Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Katholieke Universiteit Leuven, B3000 Leuven, Belgium. pushpike@med.kuleuven.be
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 PamChip(®) microarray technology. We propose a flexible semiparametric mixed model for analyzing PamChip(®) 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, 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.
Contact:
pushpike@med.kuleuven.be
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
A new statistical model analyzes PamChip(®) data to estimate protein kinase phosphorylation rates over time. This method aids in identifying therapeutic targets for diseases like cancer by comparing kinase inhibition velocities.
Area of Science:
- Biochemistry and Molecular Biology
- Bioinformatics
- Statistical Modeling
Background:
- Protein phosphorylation by kinases is vital in biological processes.
- Dysregulated kinase activity is linked to diseases, notably cancer.
- Targeting kinase activity offers therapeutic potential.
Purpose of the Study:
- To introduce a flexible semiparametric mixed model for analyzing PamChip(®) data.
- To enable estimation of phosphorylation rates (Velocity) over time with confidence intervals.
- To facilitate identification of signature peptides for protein kinase targeting.
Main Methods:
- Development of a semiparametric mixed-effects model.
- Application to PamChip(®) microarray data.
- Estimation of time-dependent phosphorylation rates (Velocity).
Main Results:
- The proposed model effectively fits kinase activity profiles from PamChip(®) data.
- Accurate estimation of kinase inhibition velocities over time was achieved.
- The model allows for testing differences in inhibition velocity between cell lines.
Conclusions:
- The semiparametric mixed model provides a robust method for analyzing PamChip(®) data.
- This approach enhances the identification of kinase activity patterns relevant to disease.
- The model supports the development of targeted therapies by quantifying kinase inhibition dynamics.
