1. Basis State Prediction of Cell-Cycle Transcription Factors in Saccharomyces cerevisiae Dr. Matteo Pellegrini Dr. Shawn Cokus Sherri Rose UCLA Molecular, Cell, and Developmental Biology Department

2. Background: Expression Analysis Microarrays measure the mRNA concentration of genes expressed within a yeast cell. Current statistical techniques to analyze microarray data: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Independent Component Analysis (ICA). These techniques do not always lead to clear interpretations because they use complicated linear combinations.

3. Rationale: Basis State Prediction Use biologically meaningful basis states. Develop a technique that will describe expression data in terms of these states.

4. Transcription Factor Binding Basis States  pharyngula.org The binding of 204 transcription factors to yeast genes was measured.

5. Expression Data Basis States Describe expression data using basis states. Y(1) = f(1)  e(1, 1) + f(2)  e(1, 2) + … + f(n)  e(1, n) Y(2) = f(1)  e(2, 1) + f(2)  e(2, 2) + … + f(n)  e(2, n).... Y(m) = f(1)  e(m, 1) + f(2)  e(m, 2) + … + f(n)  e(m, n) gene value in original experiment activity coefficient for transcription factor n binding of transcription factor n to gene 2

6. Strategy: Basis State Prediction Expression data Generated linear combinations of transcription factor binding basis states Graphical representation Analysis

7. Goal: Basis State Prediction of Cell-Cycle Dependence Predict transcription factors that are cell-cycle dependent. Compare the expression of a transcription factor to its activity.

8. Yeast Cell Cycle  http://www.tau.ac.il/ M/G1

9. Fourier Transform Fourier transform was applied to identify: 1) periodic transcription factor activity 2) mRNAs expressed in a periodic manner Data that appears to be periodic can be modeled as a sum of related sine waves. The Fourier transform decomposes a cycle of data into its sine components.

10. Results I: Transcription Factors with Periodic Activity Analysis produced a rank-ordered list of transcription factors. Some transcription factors are already known to be involved in cell cycle transcription. unknown protein transcription factor associated with stress response Not listed: ACE2

11. Results I: Comparing Transcription Factor Activity and Expression Some of the transcription factors with periodic activity do not have periodic expression levels.

12. Results I: Comparing Transcription Factor Activity and Expression Interactions Between Transcription Factors: MBP1 forms a complex with SWI6. This may explain the periodic activity of MBP1 in the cell cycle.

13. Interactions Between Transcription Factors Results I: Comparing Transcription Factor Activity and Expression Periodic Not Periodic

14. Results I: Comparing Transcription Factor Activity and Expression Identifying New Cell-Cycle Transcription Factors: YKR064W a hypothetical protein. One might hypothesize that it is periodic in the cell cycle due to unknown protein interactions.

15. Results: Prediction of Cell-Cycle Dependence What does this show? –One can use this method to identify transcription factors that are cell-cycle dependent. –One can analyze differences in expression versus activity in transcription factors.

16. Basis State Prediction: The Future The ability to describe complex expression microarray data in terms of small numbers of basis states can increase our understanding of the data and advance attempts to construct quantitative models of transcriptional networks.

17. References Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., and Futcher, B. 1998. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9: 3273-3297. Harbison, C.T., Gordon, B., Lee, T.I., Rinaldi, N.J., MacIsaac, K.D., Danford, T.W., Hannett, N.M., Tagne, J.B., Reynolds, D.B., Yoo, J., Jennings, E.G., Zeitlinger, J., Pokholok, D.K., Kellis, M., Rolfe, P.A., Takusagawa, K.T., Lander, E.S., and Gifford, D.K. 2004. Transcriptional regulatory code of a eukaryotic genome. Nature 431: 99-104.