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Integrative investigation of metabolic and transcriptomic data
Pinar Pir1, Betül Kirdar, Andrew Hayes
1Department of Chemical Engineering, Boğaziçi University, Bebek 34342, Istanbul, Turkey. pinarpir@boun.edu.tr
BMC Bioinformatics
|April 14, 2006
Summary
This study integrates yeast transcriptome and metabolic data to understand cellular responses to perturbations. Key genes in central carbon metabolism significantly influence metabolic profiles, revealing connections between gene expression and cellular function.
Area of Science:
- Systems Biology
- Metabolomics
- Transcriptomics
Background:
- Developing new analytical methods to integrate multi-omics data for systems biology.
- High-throughput methods generate large datasets requiring advanced interpretation techniques.
Purpose of the Study:
- Investigate the relationship between transcriptomic and metabolic data in yeast.
- Model metabolic profiles as a function of transcriptome profiles.
Main Methods:
- Collected transcriptomic and metabolic data from chemostat fermentors.
- Utilized linear modeling to analyze transcriptome variations under perturbations.
- Employed Partial Least Squares (PLS) to model metabolic variables using ORF expression levels.
Main Results:
- Identified open reading-frames (ORFs) with significant expression changes due to medium composition, growth rate, and gene deletions.
- Successfully modeled metabolic variables based on transcriptome data.
- Discriminated effects of growth medium, dilution rate, and gene deletions on transcriptome and metabolite profiles.
Conclusions:
- Established congruence between metabolite and transcriptome data.
- Determined that genes involved in central carbon metabolism are crucial for modeling yeast metabolic profiles.
