1 / 22

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.

idalia
Download Presentation

Joining the dots…

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 !

More Related