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Timing the Cell Cycle

Timing the Cell Cycle. Seth Berman Julian Lange Reina Riemann Ezequiel Alvarez-Saavedra. Outline. The cell cycle. A biological model. Eze phase. Seth phase. The algorithm and results. Reina phase. Julian phase. The project. Cell cycle: early findings.

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Timing the Cell Cycle

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  1. Timing the Cell Cycle • Seth Berman • Julian Lange • Reina Riemann • Ezequiel Alvarez-Saavedra

  2. Outline The cell cycle A biological model Eze phase Seth phase The algorithm and results Reina phase Julian phase The project

  3. Cell cycle: early findings • histone mRNA oscillates during the yeast cell cycle (Hereford et al, 1981) • most genes expressed at G1/S transition contain binding sequences for • specific transcription activators (Koch and Nasmyth, 1994) • many cell cycle-regulated genes are involved in processes (budding, • cytokinesis, etc.) that occur only once per cell cycle cell cycle is a complex self-regulating program

  4. Background: Spellman et al (1998) • used DNA microarrays to analyze mRNA • levels in synchronized cell cultures • identified genes whose mRNA expression • profiles were similar to those of genes known • be regulated by the cell cycle ~800 genes are cell cycle regulated

  5. Background: Simon et al (submitted) • performed genome-wide location analysis of nine known cell cycle • transcription activators • compared data to Spellman et al microarray gene expression experiments

  6. Background: Simon et al (submitted) • transcription activators function to regulate gene expression and diverse stage-specific functions during the cell cycle activators also regulate expression of the other transcription activators • leads to temporal regulation of the cell cycle

  7. Project Goal • quantitative integration of genome-wide location analysis and cell cycle • expression data to determine direct regulatory relationships among nine • transcription activators • aims: • to quantitatively validate relationships established by location analysis, with the expression data • to optimize temporal relationships based on time lags in expression • to propose a temporal model for the expression of the nine activators

  8. Activators bind at promoters of other activators Ace2 Swi5 Mbp1 Ndd1 Swi6 Mcm1 Swi4 Fkh2 Fkh1 • data from Simon et al (submitted), p=0.001

  9. Fkh1 Fkh2 Ace2 Mcm1 Ndd1 Terminology • Ace2 is a child of four parents Ace2 Swi5 Mbp1 Ndd1 Swi6 Mcm1 Swi4 Fkh2 Fkh1

  10. Cell cycle expression profiles of activators Fkh1 Fkh2 Mcm1 Ndd1 Ace2 Swi5 Mbp1 Swi4 Swi6 • data from Spellman et al (1998)

  11. Ace2: a child with four parents

  12. Data processing • naive interpolation for missing cell cycle expression data points • multivariate regression models for all time lags for each child and parents set to investigate optimal time lag and combinatorial parent relationship: child ~N(child + ∑ parents, 2) • nested likelihood ratio tests combined with F-test to validate p values

  13. Score • nested likelihood ratio tests T(X) = 2 log ((π P(child|parent,H1)/(π P(child|Ho))

  14. Algorithm For each child{ For each time lag(0 up to maximum time lag){ For each parent{ score } calculate minimum score while (number of edges in the model<number of parents){ If (score < threshold){ attempt to add another edge } } } }

  15. Results: initial network to be evaluated Ace2 Swi5 Mbp1 Ndd1 Swi6 Mcm1 Swi4 Fkh2 Fkh1

  16. Time lag: 0 minutes Ace2 Swi5 Mbp1 p=0.003 p=0.009 Ndd1 Swi6 p=0.003 Mcm1 Swi4 p=10-284 Fkh2 Fkh1

  17. Time lag: 7 minutes Ace2 Swi5 Mbp1 p=0.05 p=0.00006 Ndd1 Swi6 p=0.003 p=0.016 Mcm1 Swi4 Fkh2 Fkh1

  18. Time lag: 14 minutes Ace2 Swi5 Mbp1 p=0.001 p=0.001 Ndd1 Swi6 p=0.01 p=0.002 Mcm1 Swi4 Fkh2 Fkh1

  19. Time lag: 21 minutes Ace2 Swi5 p=0.0002 Mbp1 p=0.00002 Ndd1 Swi6 p=0.004 p=0.017 Mcm1 Swi4 Fkh2 Fkh1

  20. Time lag: 28 minutes Ace2 Swi5 Mbp1 p=0.1 p=0.02 Ndd1 Swi6 p=0.001 Mcm1 Swi4 Fkh2 Fkh1 p=0.000003

  21. Time lag: 35 minutes Ace2 Swi5 p=0.003 Mbp1 p=0.05 Ndd1 Swi6 p=0.08 p=0.007 Mcm1 Swi4 Fkh2 Fkh1

  22. Time lag: 42 minutes Ace2 Swi5 p=0.005 Mbp1 p=0.05 Ndd1 Swi6 p=0.03 p=0.007 Mcm1 Swi4 Fkh2 Fkh1

  23. Time lag: 56 minutes Ace2 Swi5 Mbp1 p=0.01 p=0.0002 Ndd1 Swi6 p=0.04 Mcm1 Swi4 Fkh2 Fkh1 p=0.008

  24. Significant edges Parents Children Time Lag 7’ Swi6 Swi4 Swi4 Ndd1 Ace2 Swi5 Swi6 Swi4 14’ 14’ Ndd1 Fkh1 Fkh2 14’ 7’ 21’ Ndd1 Fkh2 Mcm1 14’ 14’

  25. NDD1 A temporal model Swi6 Swi4 0’ 56’ 7’ 49’ 14’ 42’ 21’ Swi4 Swi6 35’ 28’ Ndd1

  26. SWI5 A temporal model 0’ 56’ 7’ 49’ 14’ 42’ 21’ Swi5 Fkh2 Ndd1 Mcm1 35’ 28’ Ndd1 Mcm1 Fkh2

  27. A biological model ? 14’-21’ Swi6 Mcm1 Swi4 Ace2 M G1 Swi5 7’-14’ 14’-21’ G2 S 21’ Fkh2 Fkh1 Ndd1 Mcm1

  28. Conclusion • initial integration of location and expression data at different time lags and proposition of a temporal cell cycle model • combine information from multiple data sources (cdc15, cdc28, elutriation, alpha-factor arrest) • build a more refined time model for each child/parent set • iteratively update the values for the missing data points Perspectives

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