Modeling the dynamic assembly of cell cycle complexes from high-throughput data
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Modeling the dynamic assembly of cell cycle complexes from high-throughput data

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EMBO Young Investigators Programme, 3rd Symposium,...

EMBO Young Investigators Programme, 3rd Symposium,
European Molecular Biology Laboratory, Heidelberg, Germany, June 24-26, 2005

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  • 1. Lars Juhl Jensen EMBL Heidelberg Modeling the dynamic assembly of cell cycle complexes from high-throughput data
  • 2. A qualitative model of the yeast cell cycle
    • Take into account the temporal nature of the cell cycle
    • Should be accurate even at the level individual interactions
    • Can we be quantitative?
    © Chen et al., Mol. Biol. Cell, 2004
  • 3. Getting the parts list Yeast culture Microarrays Gene expression Expression profile Cho & Spellman et al. 600 periodically expressed genes (with associated peak times) that encode “dynamic proteins” The parts list New analysis
  • 4. Constructing a reliable protein network
    • The stickiness of an interaction was scored based on its local network topology
    • We benchmarked these scores for each individual data set against a common reference
    • Impossible interactions were eliminated based on subcellular localization data
    • By restricting the network to a particular system the error rate is further reduced
  • 5. Extracting a cell cycle interaction network Cell cycle microarray data Physical PPI interactions with confidence scores Expand the set of proteins to include non-periodic proteins that are strongly connected to periodic proteins Raw Data Node selection List of periodically expressed proteins with peak time Interactions Require compatible compartments and high confidence Extract cell cycle network
  • 6. The temporal interaction network
    • Interacting proteins are expressed close in time
    • Two thirds of the dynamic proteins lack interactions but likely participate in transient interactions
  • 7.
    • Static proteins comprise a third of the interactions at all times of the cell cycle
    • Their time of action can be predicted from interactions with dynamic proteins
    Static proteins play a major role
  • 8. Cdc28p and its interaction partners
  • 9. Just-in-time synthesis vs. just-in-time assembly
    • Most dynamic proteins are expressed just before they are needed to carry out their function
    • Most complexes also contain static proteins
    • Just-in-time assembly of complexes appear to be the general principle
    • The time of assembly is controlled synthesizing the last subunits just-in-time
  • 10. Assembly of the pre-replication complex
  • 11. Network as a discovery tools
    • The network enables us to place 30+ uncharacterized proteins in a temporal interaction context
    • Quite detailed hypotheses can be made concerning the their function
    • The network also contains entire novel modules and complexes
  • 12. Transcription is linked to phosphorylation
    • A genome-wide screen identified 332 Cdc28p targets, which include
      • 6% of all yeast proteins
      • 8% of the static proteins
      • 27% of the dynamic ones
    • A similar correlation was observed with predicted PEST regions
    • This suggests a hitherto undescribed link between transcriptional and post-translational control
  • 13. Is it possible to predict binding affinities?
    • We would like to be able to distinguish between transient interactions and stable complexes
    • We have very recently discovered that quality scores correlate with binding affinities
    • Different evidence types suggest different types of interactions
      • Complex purification
      • Yeast two-hybrid
  • 14. Conclusions and outlook
    • What can we learn from this?
      • It is possible to construct highly reliable models from microarray data and high-throughput interaction screen
      • Temporal interaction networks can provide an overview of how and when protein complexes are assembled
      • Different mechanism for regulating protein activity appear to be tightly linked to each other
    • Where do we go now?
      • Perform comparative analysis across multiple species
      • Study other biological systems using similar approaches
      • Attempt to distinguish between transient and stable interactions
  • 15. Acknowledgments
    • The yeast cell cycle interaction network
      • Ulrik de Lichtenberg
      • Søren Brunak
      • Peer Bork
    • Re-analysis of cell cycle microarray expression data
      • Thomas Skøt Jensen
      • Anders Fausbøll
    • Also thanks to
      • Sean Hooper
      • Christian von Mering
  • 16. Thank you!