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
Van Driessche et al. Epistasis analysis with global transcriptional phenotypes. Nature Genetics  37, (2005)
Time series expression (0-24hrs) every 2hrs

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
Barcode
(UPTAG):
CTAACTC
TCGCGCA
TCATAAT
yfg1D
yfg2D
yfg3D …
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. Explaining deletion effects
This is how we display one effect, but there are 276 deletion profiles

13. 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)

14. 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).
The kinds of models
Not an original idea
Which active
Disease
(later) Consequence of loss

15. Inferring regulatory paths=
Direct =
Indirect

16. Annotate: inducer or repressor

17. Annotate: Inducer or Repressor

18. 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

19. Test & Refine

20. Inferred Network Annotations
Genes go down
GLN3?
A network with
ambiguous annotation

21. 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)

22. Target experiments to one network region
Expression for: sok2, hap4, msn4, yap6

23. Expression of Msn4 targets
Average Z-score
Msn4 target genes
Used a number of randomized trials to determine significance
Squigglies
Negative control

24. Expression of Hap4 targets
Show msn4

25. Yap6 targets are unaffected

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

27. Using this method of choosing the next experiment
Is it better than other methods?
How many experiments?
Run simulations vs:
Random
Hubs
Simulated performance of our method for picking experiments

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