1. EGRIN Session II Broadening the Model Baliga lab retreat 2010

2. Network inference with cMonkey and Inferelator Co-expression Functional Associations de novo Motif Detection Image: Christopher Plaisier

3. Information flow in the cell Images: Wikipedia and OmniGraffle

4. Transcription…

5. …plus motif detection

6. …and protein-protein interactions

7. transcription Post-transcriptional regulation post-translational modification genetics / epigenetics proteomics metabolomics Information flow in the cell

8. Data types genetic modifications environmental perturbations transcription motif detection protein-protein interactions epigenetics post-transcriptional regulation post-translational modification protein-DNA binding protein quantification metabolites ncRNAs degradation of transcript degradation of proteins

9. Current EGRIN

11. ? Effect of bicluster on metabolites

12. Can we broaden the network and prediction by laying EGRIN on top of metabolic network?

13. Causal Inference (Conditional Independence) TFEnzymeMetabolite

14. Using Causal Inference Hole filling in the metabolic network – Correct inconsistencies using causal inference Link expression of regulators to expression of genes to levels of metabolites Nice because it can be done using only SEMs based on linear regression, just like cMonkey and Inferelator

15. Conditional Independence (4/12 ) * (6/12) = 2/12 = 1/6 (2/12) = 1/6 (12/37) * (12/37) = 0.105 (4/37) = 0.108

16. Conditional Dependence http://www.stat.columbia.edu/~cook/movabletype/mlm/mapsnyt.jpg Democrat Republican