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Timing from Stochasticity Scott Yang Nick Rhind (UMass Med) John - PowerPoint PPT Presentation

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Modelling the Genome-wide Replication Program of Budding Yeast: Timing from Stochasticity Scott Yang Nick Rhind (UMass Med) John Bechhoefer (SFU) CMMT TGIF Series Dec 17, 2010 Take-home messages Should consider DNA replication from a

  1. Modelling the Genome-wide Replication Program of Budding Yeast: Timing from Stochasticity Scott Yang Nick Rhind (UMass Med) John Bechhoefer (SFU) CMMT TGIF Series Dec 17, 2010

  2. Take-home messages • Should consider DNA replication from a stochastic point of view • Precise timing of the replication program can emerge from stochasticity

  3. DNA replication http://www.paterson.man.ac.uk/cellcycle/replication.stm

  4. DNA replication: the Kinetics S phase S Non-replicated G1 Potential origins

  5. DNA replication: the Kinetics S phase S Non-replicated G1 Potential origins Origins + forks = replication program

  6. A Microarray Experiment synchronize Raghuraman et al . Science 2001

  7. Replication profiles Position x 100 % replication 0 Time (min) Raghuraman et al . Science 2001

  8. Replication profiles Replication time profile VI Raghuraman et al . Science 2001

  9. Replication profiles Position x Position x 100 100 % replication % replication 0 0 Time (min) Time (min) Raghuraman et al . Science 2001

  10. Replication profiles Replication time profile Replication fraction profile 100 VI % Replication 0 Raghuraman et al . Science 2001 McCune et al. Genetics 2008

  11. Point of Views More deterministic • Each origin has a preprogrammed firing time • plus some variation around that time

  12. Point of Views More deterministic More stochastic • Each origin has a • Each origin has a distribution of firing times preprogrammed firing time • has an expected firing • plus some variation around time that time

  13. Point of Views More deterministic More stochastic • Each origin has a • Each origin has a distribution of firing times preprogrammed firing time • has an expected firing • plus some variation around time that time • How to ensure precise firing • What counts the time and time if needed? how?

  14. Parametric model Firing-time distribution 0 100 200 Genome position (kb) x: origin position Cumulative firing-time distribution t 1/2 : median of distribution = sigmoid function t w : width of distribution v: globally constant fork velocity

  15. Parametric model Firing-time distribution 0 100 200 Genome position (kb) 100 x: origin position % rep. t 1/2 : median of distribution t w : width of distribution 0 v: globally constant fork velocity

  16. Key theoretical idea      N x x          i  f ( x , t ) 1 1 t   i     v    i 1 global fork velocity

  17. Result 1: fit McCune 2008

  18. Result 1: fit McCune 2008

  19. Result 2: firing-time distributions

  20. An idea Maybe…origins The number of MCM with wi th lot lots s of of exceeds the number MC MCM f M fire re of ORC by a factor of ear arly. y. 10 – 100 in various organisms! Nick Hyrien 2003

  21. Multiple stochastic initiators Firing-time dist. Time (min)

  22. Multiple initiator model Increasing # of initiators

  23. Point of Views More stochastic • How to ensure precise firing time if needed?

  24. Point of Views More stochastic • How to ensure precise firing time if needed? • Give it lots of MCM

  25. Point of Views More deterministic More stochastic • How to ensure precise firing • What counts the time and time if needed? how? • Give it lots of MCM

  26. Point of Views More deterministic More stochastic • How to ensure precise firing • What counts the time and time if needed? how? • Give it lots of MCM • ????

  27. Conclusions • DNA replication is a stochastic process • We have developed a flexible, analytical model • Timing needs not be from an explicit clock (contrary to most biologists’ intuitions?) • Timing can emerge from multiple stochastic initiators (MCM2 – 7) Yang, Rhind, Bechhoefer, MSB 2010

  28. Current work • Probe MCM occupancy and other factors • Other experimental setups & techniques • Other organisms  universal program? Molecular Systems Biology 6 :404 (2010) Thank you!

  29. Toy replication fraction profile A culture of cells T minutes into S phase 1 origin Average 100 % rep + + + + … 0 position Firing-time distribution

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