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Inferring yeast cell cycle regulators and interactions using transcription factor activities.
Young-Lyeol Yang1, Jason Suen, Mark P Brynildsen
1Department of Chemical Engineering, University of California, Los Angeles 90095, USA. ylyang@ucla.edu
BMC Genomics
|June 14, 2005
Summary
Network Component Analysis (NCA) infers transcription factor activities (TFAs) to reveal cell cycle regulators and interactions in yeast. This method identifies known and novel factors, enhancing our understanding of gene regulation.
Area of Science:
- Systems Biology
- Molecular Biology
- Genomics
Background:
- Transcription factor activity, not just expression, is crucial for understanding cellular functions.
- Network Component Analysis (NCA) and its generalized form (gNCA) offer a robust method to infer transcription factor activities (TFAs) from microarray data.
- This study focuses on applying TFAs to elucidate transcription factor roles in Saccharomyces cerevisiae cell cycle regulation.
Purpose of the Study:
- To demonstrate the utility of TFAs in identifying transcription factor functions.
- To infer transcription factor interactions within the context of the yeast cell cycle.
- To uncover novel regulators and interaction partners involved in cell cycle control.
Main Methods:
- Generalized Network Component Analysis (gNCA) was employed to calculate 74 TFAs from microarray data of wild type and fkh1 fkh2 deletion mutant yeast strains.
- Cluster and periodicity analyses were applied to TFA profiles to identify cyclic activity patterns characteristic of cell cycle regulators.
- Analysis of transcription factor knockout strains' expression data was used to determine interaction partners.
Main Results:
- TFAs revealed cyclic activity profiles for known cell cycle regulators, supporting the study's hypothesis.
- Nearly 90% of known cell cycle regulators were recovered, and 5 putative novel cell cycle-related transcription factors were identified.
- Three verified and four putative interaction partners of forkhead transcription factors were determined, with confidence levels assessed based on sensitivity analysis.
Conclusions:
- TFA profiles, analyzed using physiological signatures, successfully identified known cell cycle regulators.
- The study identified transcription factors with potential cell cycle-dependent roles and elucidated interactions between transcription factors.
- This approach provides a reliable method for discovering transcription factor functions and interactions in complex biological processes like the cell cycle.