Modeling the Network of Cell Cycle Transcription Factors in Yeast
Author Information
Author(s): Cokus Shawn, Rose Sherri, Haynor David, Grønbech-Jensen Niels, Pellegrini Matteo
Primary Institution: University of California, Los Angeles
Hypothesis
Can we reverse-engineer the network of relationships among transcription factors that regulate the cell cycle in Saccharomyces cerevisiae?
Conclusion
The regression-based approach allows for a comprehensive model of transcriptional regulation of the yeast cell cycle, including previously unreported factors and interactions.
Supporting Evidence
- The model accurately predicts transcription factor activities during the yeast cell cycle.
- New factors were identified that may play significant roles in cell cycle regulation.
- The model's predictions align with previously published models of transcriptional regulation.
Takeaway
This study created a model to understand how different proteins work together to control the yeast cell cycle, helping scientists learn more about how cells grow and divide.
Methodology
The study used linear regression to estimate transcription factor activities from expression time series and binding data, followed by least squares to model their time evolution.
Potential Biases
Potential biases may arise from the selection of transcription factors based on p-values and the assumptions made in the modeling process.
Limitations
The model may not capture all interactions due to the complexity of transcriptional regulation and reliance on available data.
Statistical Information
P-Value
p<0.1
Statistical Significance
p<0.1
Digital Object Identifier (DOI)
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