1. Functional genomics and inferring regulatory pathways with gene expression data

2. Principle of Epistasis Analysis Determines order of influence Used to reconstruct pathways

3. Experimental Design: Single vs Double-Gene Deletions

4. Epistasis Analysis Using Microarrays to Determine the Molecular Phenotypes Time series expression (0-24hrs) every 2hrs Van Driessche et al. Epistasis analysis with global transcriptional phenotypes. Nature Genetics 37, 471 - 477 (2005)

5. Pathway Reconstruction Expression data Known pathway Inferred pathway

6. Expression Profiling in 276 Yeast Single-Gene Deletion Strains “The Rosetta Compendium” Only 19 % of yeast genes are essential in rich media, Giaever et. al. Nature (2002)

7. Clustered Rosetta Compendium Data

8. Gene Deletion Profiles Identify Gene Function and Pathways

10. Systematic phenotyping yfg1  yfg2  yfg3  CTAACTCTCGCGCATCATAAT Barcode (UPTAG): Deletion Strain: Growth 6hrs in minimal media (how many doublings?) Rich media … Harvest and label genomic DNA

11. Microarrays for functional genomics Hillenmeyer M, et al., Science 2008

12. Pathway reconstruction

15. Chen-Hsiang Yeang, PhD Craig Mak MIT UCSD UC Santa Cruz Yeang, Jaakkola, Ideker. J Comp Bio (2004)

16. Explaining deletion effects

17. Relevant Relationships (that need to be explained) Rosetta compendium used 28 deletions were TF (red circles) –355 diff. exp. genes (white boxes) –P < 0.005 –755 TF-deletion effects (grey squiggles)

18. Evidence for pathway inferrence Step 1: Physical Interaction Network –Y2H, chIP-chip Step 2: Integrate state data –Measure variables that are a function of the network (gene expression) –Monitor these effects after perturbing the network (TF knockouts).

19. Inferring regulatory paths = = Direct Indirect

20. Annotate: inducer or repressor OR

21. Annotate: Inducer or Repressor

22. Computational methods Problem Statement: –Find regulatory paths consisting of physical interactions that “explain” functional relationship Method: –A probabilistic inference approach –Yeang, Ideker et. al. J Comp Bio (2004) To assign annotations Formalize problem using a factor graph Solve using max product algorithm –Kschischang. IEEE Trans. Information Theory (2001) –Mathematically similar to Bayesian inference, Markov random fields, belief propagation

23. Inferred Network Annotations A network with ambiguous annotation

24. Inferring Regulatory Role 50/132 protein-DNA interactions had been confirmed in low- throughput assays (Proteome BioKnowledge Library) Inferred regulatory roles (induction or repression) for 48 out of 50 of these interactions agreed with their experimentally determined roles. (96%, binomial p-value < 1.22 × 10-7)

25. Test & Refine

26. Which deletion experiments should we do next? A mutual information based score –For each candidate experiment (gene  ) Variability of predicted expression profiles –Predict profile for each possible set of annotations –More variation = more information from experiment Reuse network inference algorithm to compute effect of deletion.

27. Ranking candidate experiments

28. Target experiments to one network region Expression for: SOK2 , HAP4 , MSN4 , YAP6 

29. Expression of Msn4 targets Average Z-score Negative control

30. Expression of Hap4 targets

31. Yap6 targets are unaffected

32. Refined Network Model Caveats –Assumes target genes are correct –Only models linear paths –Combinatorial effects missed –Measurements are for rich media growth

33. Using this method of choosing the next experiment Is it better than other methods? How many experiments? Run simulations vs: –Random –Hubs

34. Simulation results # simulated deletions profiles used to learn a “true” network