Network Biology Lent 2010 - lecture 1

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

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Network Biology Lent 2010 - lecture 1

  1. 1. Perturbations and phenotypes - an introduction - Network Biology - lecture 1
  2. 3. Today’s lecture <ul><li>What are phenotyping screens? </li></ul><ul><ul><li>Perturbations : how to knock-out or knock-down a gene? </li></ul></ul><ul><ul><li>Phenotypes : how to characterize the effects of a perturbation on the cell or organism? </li></ul></ul><ul><li>Where does computation come in? </li></ul><ul><li>What challenges do we have to deal with? </li></ul>
  3. 4. How to understand a complex system? <ul><li>Functional Genomics : </li></ul><ul><li>“ What I cannot break , I do not understand.” </li></ul><ul><li>experimental interventions </li></ul><ul><li>gene perturbation screens </li></ul><ul><li>Richard Feynman : </li></ul><ul><li>“ What I cannot create , I do not understand.” </li></ul><ul><li>Synthetic biology </li></ul><ul><li>Mathematical models in systems biology </li></ul>
  4. 5. Breaking the system DNA mRNA Protein RNAi Knockout Drugs Small molecules Stress SNPs natural variation
  5. 8. Phenotype: viability versus cell death <ul><li>Interpretation: </li></ul><ul><li>non-essential gene </li></ul><ul><li>redundancy </li></ul>Interpretation: an essential gene for the organism WT B- A-
  6. 9. Science 1999
  7. 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!
  8. 11. Nature Reviews Genetics 2008
  9. 12. RNAi screens Boutros and Ahringer (2008)
  10. 13. Phenotype: pathway activity Cell membrane Receptors A- B- C-
  11. 14. Phenotype: organism morphology Boutros and Ahringer, Nat Rev 2008
  12. 15. Phenotype: cell morphology Boutros and Ahringer, Nat Rev 2008
  13. 16. Phenotype: global gene expression A- B- C- All the genes in the genome Transcriptional phenotypes by microarrays A- B- C- … … …
  14. 17. High-dimensional phenotypes by microscopy or molecular profiling Low-dimensional phenotypes A- Time Size
  15. 18. Phenotyping produces partslists Keith Haring, Untitled , 1986 Urs Wehrli, Tidying Up Art , 2003
  16. 19. <ul><li>Low-level analysis </li></ul><ul><ul><li>Image analysis </li></ul></ul><ul><ul><li>Normalization </li></ul></ul><ul><li>Data integration </li></ul><ul><li>Network reconstruction </li></ul><ul><li>Prediction </li></ul>A challenge for computation and statistics
  17. 21. Boutros, Bras, Huber 2006
  18. 22. Boutros, Bras, Huber 2006
  19. 23. Boutros and Ahringer 2008
  20. 24. Challenges <ul><li>Perturbations can have off-target effects </li></ul><ul><li>A single perturbation may not be 100% effective </li></ul><ul><li>Compensatory efforts of the cell may mask phenotypes </li></ul><ul><li>Phenotypes give only limited information of gene networks and pathways </li></ul>
  21. 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
  22. 26. A single perturbation may not be 100% effective Boutros and Ahringer (2008) Effectiveness depends eg on degree of sequence similarity <ul><li>Long dsRNA </li></ul><ul><li>many interfering RNAs </li></ul><ul><li>siRNA in Humans: </li></ul><ul><li>only single </li></ul><ul><li>interfering RNA </li></ul><ul><li>always use more than one </li></ul>
  23. 27. Compensatory mechanisms in the cell may mask phenotypes <ul><li>If two (or more) pathways regulate the same function, you will need more than one perturbation to see a phenotype! </li></ul>
  24. 28. Phenotypes give only limited information of cellular networks
  25. 29. .. even molecular phenotypes Model DNA mRNA Protein A B C D E
  26. 30. Example on real data <ul><li>Pheromone response pathway in Yeast </li></ul><ul><li>Gene expression data from Roberts (2000) </li></ul><ul><li>… same observations in Hughes et al (2000) </li></ul>
  27. 31. Today’s lecture <ul><li>What are phenotyping screens? </li></ul><ul><ul><li>Perturbations : how to knock-out or knock-down a gene? </li></ul></ul><ul><ul><li>Phenotypes : how to characterize the effects of a perturbation on the cell or organism? </li></ul></ul><ul><li>Where does computation come in? </li></ul><ul><li>What challenges do we have to deal with? </li></ul>
  28. 32. Three take-home messages <ul><li>Phenotyping screens are key technologies in functional genomics </li></ul><ul><li>Experimental perturbations are mostly knock-outs or knock-downs, phenotypes are various from viability and morphology to global gene expression. </li></ul><ul><li>Even molecular phenotypes only show a ‘slice’ of what is happening in the cell. </li></ul>
  29. 33. Overview of rest of lectures <ul><li>Enrichment analysis, clustering and ranking </li></ul><ul><ul><li>function prediction by guilt-by-association </li></ul></ul><ul><li>Perturbations in probabilistic graphical models </li></ul><ul><ul><li>Inferring Bayesian networks from perturbation data </li></ul></ul><ul><li>Multiple-Input Multiple-Output models </li></ul><ul><ul><li>Non-linear dynamic models </li></ul></ul><ul><li>Nested Effects Models </li></ul><ul><ul><li>Inferring pathways from down-stream effects </li></ul></ul><ul><li>Genetic interaction networks </li></ul><ul><ul><li>Combinatorial perturbations </li></ul></ul>
  30. 34. Network Biology - lecture 1 Questions ?

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