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Simultaneous modeling of concentration-effect and time-course patterns in gene expression data from microarrays
Yseult F Brun1, Ram Varma, Suzanne M Hector
1Cancer Prevention and Population Sciences, Roswell Park Cancer Institute, Buffalo, NY 14263, USA.
Cancer Genomics & Proteomics
|March 25, 2008
Summary
This study introduces a new method for analyzing complex gene expression data from time-course and drug concentration experiments. The approach helps distinguish gene expression patterns, aiding in understanding drug mechanisms and action timing.
Area of Science:
- Genomics
- Bioinformatics
- Pharmacology
Background:
- Microarray studies often use limited experimental designs (treated vs. control).
- Complex time-course and concentration-effect experiments yield richer data but pose analytical challenges.
Purpose of the Study:
- To develop a semi-automated method for simultaneously fitting time profiles and concentration-effect patterns in gene expression data.
- To enable more comprehensive analysis of complex experimental designs.
Main Methods:
- Implemented a semi-automated method integrating exponential models (for time-course) and a 4-parameter Hill model (for concentration-effect).
- Applied the method to Affymetrix HG-U95Av2 data from platinum drug (cisplatin, oxaliplatin) treatment of ovarian carcinoma cells.
- Simultaneously modeled time-course and concentration-effect for 18 selected genes.
Main Results:
- Successfully applied the method to a dataset of 51 arrays.
- The analysis distinguished genes with different expression patterns between cisplatin and oxaliplatin treatments.
- Model parameters provided insights into gene behavior across time and drug concentrations.
Conclusions:
- The developed method effectively analyzes complex gene expression data.
- This approach aids in understanding molecular mechanisms and the temporal dynamics of drug actions.
- Facilitates deeper insights into drug responses compared to simpler experimental designs.
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