Phenotyping screens involve experimentally perturbing genes through knockouts or knockdowns and characterizing the effects on cells or organisms. Perturbations can have off-target effects or not be fully effective due to compensatory mechanisms, and phenotypes only provide limited insight into networks. Computational analysis is needed to integrate phenotype data and infer networks. Challenges include masking effects and phenotypes only revealing one aspect of cellular changes.

1. Perturbations and phenotypes - an introduction - Network Biology - lecture 1

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

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

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 ?