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Joining the dots…

Joining the dots…. Network analysis of gene perturbation data. How to understand a complex system?. M. mycoides JCVI-syn1.0. Richard Feynman : “ What I cannot create , I do not understand. ”. Functional Genomics : “ What I cannot break , I do not understand. ”. Breaking the system.

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Joining the dots…

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  1. Joining the dots… Network analysis of gene perturbation data

  2. How to understand a complex system? M. mycoides JCVI-syn1.0 Richard Feynman: “What I cannot create, I do not understand.” Functional Genomics: “What I cannot break, I do not understand.”

  3. Breaking the system Drugs Small molecules RNAi Protein Stress Knockout Pathway mRNA DNA Somatic aberrations

  4. Today’s lecture • What information do we get out of gene perturbations? • Phenotypes and their ‘richness’ • How do we use this information to infer the internal architecture of a cell? • Guilt-by-association • Nested Effects Models

  5. Phenotype: viability versus cell death A- WT B-

  6. Phenotype: organism morphology Boutros and Ahringer, Nat Rev 2008

  7. Phenotype: cell morphology After gene silencing RNAi control Boutros and Ahringer, Nat Rev 2008

  8. Phenotype: pathway activity B- A- C- Receptors

  9. Phenotype: global gene expression B- A- C- … … … All the genes in the genome A- B- C- Transcriptional phenotypes by microarrays

  10. Phenotyping produces partslists Urs Wehrli, Tidying Up Art, 2003 Keith Haring, Untitled, 1986

  11. A challenge for computation and statistics

  12. From phenotypes to clusters A B A B C Guilt by association

  13. From clusters to mechanisms ?? A B A B B A A B A B A B

  14. Nested Effects Models TF1 TF2 Kinase TF3 TF1 TF2 Nested effect models: subset relations Guilt-by-assocation: similarity Markowetz et al 2005, 2007 Tresch and Markowetz 2008

  15. Nested Effects Models A • Set of candidate pathway genes • High-dimensional phenotypic profile, e.g. microarray B INPUT C D A B E Graph explaining the phenotypes OUTPUT F G E F C D H Phenotypic profiles Inferred pathway G H Gene perturbations Effects

  16. Anatomy of the NFB pathway ? Hits Weak Strong Phenotype Knock-down Compare expression phenotypes by NEMs Known pathway members New RNAi Hits Step 1 Roland Schwarz + Meyer lab @ MPI IB Berlin NFB Step 2

  17. Nested Effect Models for NFB Roland Schwarz

  18. Take-home messages • Phenotyping screens probe a cell’s reaction to targeted perturbations • Guilt-by-assocation is a powerful predictor of gene/protein function … • … but Guilt-by-assocation has limited ability to infer mechanisms • Inferring subset relations by Nested Effects Models provides hierarchical view of cellular organisation

  19. PLoS Comput Biol 6(2) 2010

  20. the team

  21. Joining the dots … Thank you !

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