1. Achim Tresch Computational Biology Gene Center Munich (The Sound of One-Hand Clapping) Modeling Combinatorial Intervention Effects in Transcription Networks

2. The Question (Japanese Kōan) If two hands clap and there is a sound; what is the sound of one hand? If two hands clap and there is a sound; what is the sound of one hand? Kōan A paradoxical anecdote or riddle, used in Zen Buddhism to demonstrate the inadequacy of logical reasoning and to provoke enlightenment.

3. Synthetic Genetic Interactions modified after Collins, Krogan et al., Nature 2007 How to define “Interaction“ mathematically? Synthetic Genetic Array ΔAΔA GrowthY A of single manipulation of A ΔBΔB GrowthY B of single manipulation of B ΔAΔBΔAΔB Growth Y AB of double manipulation of A and B

4. Synthetic Genetic Interactions ΔBΔBΔAΔA ΔAΔBΔAΔB Phenotype Measurement Y A of single perturbation Phenotype Measurement Y B of single perturbation Phenotype Measurement Y AB of double perturbation How to define “Interaction“ mathematically? The interaction score S AB is a function of the two single perturbations and the combined perturbation, S AB = S AB (Y A,Y B,Y AB )

5. Synthetic Genetic Interactions Common Interaction Scores Common choices for f : f = min(Y A,Y B ) (v. Liebig´s minimum rule for plant growth) f = Y A ·Y B (chemical equilibrium a + b ↔ ab, [a][b] = [ab]) f = Y A + Y B (log version of Y A ·Y B ) f = log 2 [(2 Y A - 1)(2 Y B - 1) + 1] (essentially the same as Y A + Y B ) Define an expected phenotype of the double perturbation as a function f(Y A,Y B ) of the single perturbation phenotypes Y A and Y b. The interaction score S AB is then the deviation from the expected phenotype S AB = Y AB - f(Y A,Y B ) Interaction Scores are not very reliable Results crucially depend on f Mani, Roth et al., PNAS 2007

6. Synthetic Genetic Interactions Pan, Boeke et al., Cell 2006 Cartoon by Van de Peppel et al, Mol. Cell 2005 Collins, Krogan et al., Nature 2007 Breakthrough: Combine a set of weak predictors to create a strong predictor (guilt by association = correlation of interaction scores)

7. Synthetic Genetic Interactions Costanzo M, Myers CL, Andrews BJ, Boone C, et al.: Science 2010 Take home message: Two components are likely to interact (physically) whenever they have the same interaction partners

8. Screening for TF interactions ΔAΔA One manipulation High dimensional readout If two hands clap and there is a sound; what is the sound of one hand? If two hands clap and there is a sound; what is the sound of one hand?

9. Genetic interactions from one perturbation Harbison, Fraenkel, Young et al. Nature 2004 MacIsaac, Fraenkel et al. BMC Bioinformatics 2006 Ansari et al., Nature Methods 2010 Berger, Bulyk et al., Nature Biotech 2006 a) From ChIP binding experiments b) From protein binding arrays, followed by PWM-based predictions Step 1: Construct a transcription factor - target graph

10. Genetic interactions from one perturbation Step 1: Construct a transcription factor - target graph Intersection size of target sets of TF1 and TF2 can be used alone to assess TF cooperativity. (Beyer, Ideker et al., PlOS Comp. Biol 2006)

11. Genetic interactions from one perturbation ~2.000 target genes 118 transcription factors Graph obtained from MacIsaac et al. (BMC Bioinformatics 2006) Established Methods for the detection of univariate TF activity : GSEA (Subramanian, Tamayo PNAS 2005) Globaltest (Goemann, Bioinformatics 2004) MGSEA (Bauer, Gagneur, Nucl. Acids Res. 2010) and many more … Step 2: Combine TF-target information and expression data Common Idea: A TF is active if its set of target genes shows significantly altered expression. To quantify this, various tests are constructed.

12. gene 4 time Antagonistic interaction of TF 1+2 TF 1+2 active Genetic interactions from one perturbation gene 1 TF 1TF 2 Synthesis rates during salt stress gene 2 TF 1 is active gene 3 TF 2 is active Binding sites TF1 TF2 Step 3: Given TF1 and TF2, group genes into 4 interaction classes

13. Genetic interactions from one perturbation gene 1 TF 2 is inactive gene 3 TF 1 is inactive gene 2 TF 1TF 2 Synthesis rates during salt stress Binding sites time Synergistic interaction of TF1+2 gene 4 TF 1+2 active Step 3: Given TF1 and TF2, group genes into 4 interaction classes

14. Genetic interactions from one perturbation Our interaction score for the pair (T 1,T 2 ) is then β 12. Step 4: Use these 4 groups to define an interaction score (for all genes g) For any pair of transcription factors T 1 and T 2, we perform a logistic regression.

15. Genetic interactions from one perturbation gene 1 gene 3 gene 2 gene 4 time TF 1 is active TF 2 is active Antagonistic interaction Example: TF 1+2 active Step 4: Use these 4 groups to define an interaction score TF 1TF 2 Binding sites

16. Application: Osmotic stress in yeast Use the guilt by association trick to construct an interaction matrix for all transcription factors using only a two group microarray comparison! Inclusion criterion: only TFs with >70 targets Miller, Tresch, Cramer et al., Mol. Syst. Biol. 2010, in revision „One hand clapping“

17. Validation with BioGRID database: Application: Osmotic stress in yeast Among 84 TFs under consideration (with enough targets), 3486 potential interactions Exist. Only 97 interactions are recorded.

18. Application: Osmotic stress in yeast Single interactions scores don‘t work well Profile correlations do work Validation with BioGRID database:

19. Genetic interactions from one intervention One hand clapping can be applied to: Microarray data, Pol II ChIP data, nascent RNA data 3 stress responses: osmotic stress NaCl, osmotic stress KCl, heat shock (Mitchell, Pilpel at al. Nature 2009): Application to a similar dataset leads to similar results:

20. Acknowledgements 20 Gene Center Munich: Patrick Cramer Dietmar Martin Björn Schwalb Sebastian Dümcke

21. My Answer Two hands clap and there is a sound; what is the sound of one hand? Two hands clap and there is a sound; what is the sound of one hand? It is similar for transcription factors that interact. Zen BiologySystems Buddhism