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Published on: June 14, 2013
Identification and modeling of genes with diurnal oscillations from microarray time series data
Wenxue Wang1, Bijoy K Ghosh, Himadri B Pakrasi
1Institute for Collaborative Biotechnologies, University of California, Santa Barbara, 1223 Elings Hall, Santa Barbara, CA 93106-5100, USA. wenxue@engineering.ucsb.edu
This study analyzes Cyanothece transcriptome data to identify rhythmic gene expression patterns. Findings reveal specific metabolic genes cluster by phase, suggesting preferred times for cellular functions, and propose a model for circadian-controlled genes.
Area of Science:
- Microbiology
- Systems Biology
- Genomics
Background:
- Organismal behavior is influenced by daily cycles, leading to circadian rhythms.
- Circadian rhythms are biological phenomena crucial for coordinating activities between day and night.
Purpose of the Study:
- To analyze transcriptome data from Cyanothece to discover rhythmically expressed genes.
- To cluster genes based on expression phases and identify functional groups with preferred activity times.
- To develop a model approximating circadian-controlled genes.
Main Methods:
- Transcriptome data analysis of Cyanothece.
- Gene clustering based on expression phase.
- Application of analysis to microarray experiments under varying light conditions.
- Development of a phase oscillator network model.
Main Results:
- Identified genes with rhythmic expression patterns in Cyanothece.
- Discovered tight clustering of specific metabolic genes by phase, indicating temporal specialization.
- Successfully approximated circadian-controlled genes using a proposed network model.
Conclusions:
- Circadian rhythms in Cyanothece involve coordinated temporal expression of metabolic genes.
- The proposed model effectively approximates circadian-controlled gene sets.
- Rhythmic gene expression is a key mechanism for temporal organization in organisms.
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