1. Structure Learning for Inferring a Biological Pathway Charles Vaske Stuart Lab

2. Biological Pathways Cell is a dynamical system Somewhat modularized (into pathways) Given pathway elements, how do they communicate? –Protein modification –Gene expression changes

3. Input Set of genes essential to phenotype RNAi perturbation - gene knockdown Expression measurement S Genes - essential to phenotype - each is individually perturbed E Genes - affected by S Genes - expression is measured

4. Input E Genes S genes & controls

5. Desired Output: Structure Probabilistic Model Binary Variable Domain Restricted factor form –Deterministic signalling –Shared measurement error rates Markowetz, et al. 2005

6. Model Averaging Maximum Likelihood estimate might not be interesting Gain a posterior on particular model features

7. First Attempt: All linear models Calculate likelihood of data under each model Find posterior of individual edges Tiered Sgenes (matches initial discovery method)

8. Link Robustness Noise level estimated from replicate spots Added noise to data, reran experiment 10 times

9. Link Significance

10. Experimentally verified

11. Scaling to More Sgenes All linear permutations is a hack MCMC structure sampling?