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[Estimating coarse gene networks from yeast gene expression time series]
Xiao-Jiang Xu1, Lian-Shui Wang, Da-Fu Ding
1Key Laboratory of Proteomics, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, the Chinese Academy of Sciences, Shanghai 200031, China.
Sheng Wu Hua Xue Yu Sheng Wu Wu Li Xue Bao Acta Biochimica Et Biophysica Sinica
|August 5, 2003
Summary
This study introduces a novel approach to decipher yeast gene networks using linear transcriptional modeling and gene co-expression clustering. The method aids in understanding gene expression regulation in response to environmental stress.
Area of Science:
- Systems Biology
- Molecular Biology
- Bioinformatics
Context:
- Gene networks represent gene-gene regulatory relationships at the expression level.
- Cellular adaptation to environmental changes involves complex genomic expression reorganization.
- Environmental stress significantly impacts gene expression patterns.
Purpose:
- To develop and apply a combined approach for deciphering yeast gene networks.
- To integrate linear transcriptional modeling, promoter element identification, and gene co-expression clustering.
- To analyze gene expression time series data for understanding regulatory mechanisms.
Summary:
- A novel computational approach combining linear transcriptional modeling, promoter element identification, and gene co-expression clustering was developed to decipher yeast gene networks.
- The study analyzed gene expression time series data, revealing insights into cellular responses to environmental stress.
- Specific transcription factors, Mcm1 and Dal82, were identified with their corresponding binding sites involved in cell cycle progression and environmental stress response.
Impact:
- The developed approach offers a valuable tool for modeling gene networks from microarray data.
- Provides a deeper understanding of gene regulatory mechanisms in yeast.
- Contributes to the field of systems biology by offering new methods for network inference.