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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 • 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 • 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
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
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
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
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
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
Fkh1 Fkh2 Ace2 Mcm1 Ndd1 Terminology • Ace2 is a child of four parents Ace2 Swi5 Mbp1 Ndd1 Swi6 Mcm1 Swi4 Fkh2 Fkh1
Cell cycle expression profiles of activators Fkh1 Fkh2 Mcm1 Ndd1 Ace2 Swi5 Mbp1 Swi4 Swi6 • data from Spellman et al (1998)
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
Score • nested likelihood ratio tests T(X) = 2 log ((π P(child|parent,H1)/(π P(child|Ho))
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 } } } }
Results: initial network to be evaluated Ace2 Swi5 Mbp1 Ndd1 Swi6 Mcm1 Swi4 Fkh2 Fkh1
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
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
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
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
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
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
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
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
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’
NDD1 A temporal model Swi6 Swi4 0’ 56’ 7’ 49’ 14’ 42’ 21’ Swi4 Swi6 35’ 28’ Ndd1
SWI5 A temporal model 0’ 56’ 7’ 49’ 14’ 42’ 21’ Swi5 Fkh2 Ndd1 Mcm1 35’ 28’ Ndd1 Mcm1 Fkh2
A biological model ? 14’-21’ Swi6 Mcm1 Swi4 Ace2 M G1 Swi5 7’-14’ 14’-21’ G2 S 21’ Fkh2 Fkh1 Ndd1 Mcm1
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