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Network Biology Lent 2010 - lecture 1

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Lent 2010 MPhil course \\\'Network biology\\\' - lecture 1

Lent 2010 MPhil course \\\'Network biology\\\' - lecture 1


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  • 1. Perturbations and phenotypes - an introduction - Network Biology - lecture 1
  • 2.  
  • 3. Today’s lecture
    • What are phenotyping screens?
      • Perturbations : how to knock-out or knock-down a gene?
      • Phenotypes : how to characterize the effects of a perturbation on the cell or organism?
    • Where does computation come in?
    • What challenges do we have to deal with?
  • 4. How to understand a complex system?
    • Functional Genomics :
    • “ What I cannot break , I do not understand.”
    • experimental interventions
    • gene perturbation screens
    • Richard Feynman :
    • “ What I cannot create , I do not understand.”
    • Synthetic biology
    • Mathematical models in systems biology
  • 5. Breaking the system DNA mRNA Protein RNAi Knockout Drugs Small molecules Stress SNPs natural variation
  • 6.  
  • 7.  
  • 8. Phenotype: viability versus cell death
    • Interpretation:
    • non-essential gene
    • redundancy
    Interpretation: an essential gene for the organism WT B- A-
  • 9. Science 1999
  • 10. Evidence for network redundancy Fitness of single mutant (relative to wild-type cell) Fitness of yeast deletion mutants (~6000 different gene-level perturbations) 1800 1600 1400 1200 1000 800 600 400 200 0 Winzeler EA et al Science 1999 { ~80% can be independently deleted with no severe fitness defect!
  • 11. Nature Reviews Genetics 2008
  • 12. RNAi screens Boutros and Ahringer (2008)
  • 13. Phenotype: pathway activity Cell membrane Receptors A- B- C-
  • 14. Phenotype: organism morphology Boutros and Ahringer, Nat Rev 2008
  • 15. Phenotype: cell morphology Boutros and Ahringer, Nat Rev 2008
  • 16. Phenotype: global gene expression A- B- C- All the genes in the genome Transcriptional phenotypes by microarrays A- B- C- … … …
  • 17. High-dimensional phenotypes by microscopy or molecular profiling Low-dimensional phenotypes A- Time Size
  • 18. Phenotyping produces partslists Keith Haring, Untitled , 1986 Urs Wehrli, Tidying Up Art , 2003
  • 19.
    • Low-level analysis
      • Image analysis
      • Normalization
    • Data integration
    • Network reconstruction
    • Prediction
    A challenge for computation and statistics
  • 20.  
  • 21. Boutros, Bras, Huber 2006
  • 22. Boutros, Bras, Huber 2006
  • 23. Boutros and Ahringer 2008
  • 24. Challenges
    • Perturbations can have off-target effects
    • A single perturbation may not be 100% effective
    • Compensatory efforts of the cell may mask phenotypes
    • Phenotypes give only limited information of gene networks and pathways
  • 25. Perturbations can have off-target effects www.dharmacon.com: Tech Report: Off-target effects: Disturbing the silence of RNAi Echeverri et al, Nature Methods 2006
  • 26. A single perturbation may not be 100% effective Boutros and Ahringer (2008) Effectiveness depends eg on degree of sequence similarity
    • Long dsRNA
    • many interfering RNAs
    • siRNA in Humans:
    • only single
    • interfering RNA
    • always use more than one
  • 27. Compensatory mechanisms in the cell may mask phenotypes
    • If two (or more) pathways regulate the same function, you will need more than one perturbation to see a phenotype!
  • 28. Phenotypes give only limited information of cellular networks
  • 29. .. even molecular phenotypes Model DNA mRNA Protein A B C D E
  • 30. Example on real data
    • Pheromone response pathway in Yeast
    • Gene expression data from Roberts (2000)
    • … same observations in Hughes et al (2000)
  • 31. Today’s lecture
    • What are phenotyping screens?
      • Perturbations : how to knock-out or knock-down a gene?
      • Phenotypes : how to characterize the effects of a perturbation on the cell or organism?
    • Where does computation come in?
    • What challenges do we have to deal with?
  • 32. Three take-home messages
    • Phenotyping screens are key technologies in functional genomics
    • Experimental perturbations are mostly knock-outs or knock-downs, phenotypes are various from viability and morphology to global gene expression.
    • Even molecular phenotypes only show a ‘slice’ of what is happening in the cell.
  • 33. Overview of rest of lectures
    • Enrichment analysis, clustering and ranking
      • function prediction by guilt-by-association
    • Perturbations in probabilistic graphical models
      • Inferring Bayesian networks from perturbation data
    • Multiple-Input Multiple-Output models
      • Non-linear dynamic models
    • Nested Effects Models
      • Inferring pathways from down-stream effects
    • Genetic interaction networks
      • Combinatorial perturbations
  • 34. Network Biology - lecture 1 Questions ?

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