1. System Biology October 2013 Gustavo de Souza IMM, OUS

2. Scrutinizing global changes

3. Reductionism VS Holism The study of biology through systematic perturbation, global read-out of the multifaceted and integration of these data to formulate predictive models Ideker et al., Annu. Rev. Genomics Hum. Genet. 2:343-72, 2001.

4. Integrating “omic” approaches Ge H et al., 2003.

5. Integrating expression data Ge H et al., 2003.

6. Comparing data Ge H et al., 2003.

7. Strategy Ge H et al., 2003.

8. System Biology in Immunology Amit I et al., 2011

9. System Biology in Immunology Amit I et al., 2011

10. System Biology in Immunology

11. Obvious Challenges - Signal VS Noise - Biologically relevant VS random effect - Integration of technology – biology - computation - Integration of discovery- and hypothesis-driven science

12. Proteomics capacity as a System Biology tool

14. Proteomics can reach 86% of Ids predicted by transcriptomics At the cost of 12 days of analysis

15. Characterization of 11 cell lines Geiger T et al., 2012

16. Characterization of 11 cell lines

18. Proteomics flex its muscle Mann et al., 2013

19. Take home message 1.System biology is a post-genomic field that focus in global changes within a system. 2.Depends heavily on computational approaches (measurement, statistical validation, integration of thousands of components) 3.Proteomics is still “under development” compared to Genomics/transcriptomics, but has almost comparable throughput in recent years

20. Advanced Proteomics October 2013 Gustavo de Souza IMM, OUS

21. Are mRNA levels comparable to their protein levels?

22. Relevance of the Proteome If a gene is transcripted and its mRNA levels goes up, does that necessarily means the protein level that the mRNA translates also goes up?

23. Pulse Labeling

25. Translational machinery saturates

26. Steen and Mann, 2004 Can one predict protein levels based on mRNA levels?

30. Super-SILAC Geiger T et al., 2010

31. Spike-In SILAC Geiger T et al., 2013

32. Good reproducibility

33. Top 100 Proteome

36. Brown VS White fat tissue

37. mRNA VS Protein level

38. Take home message - As a descriptive data, proteomics is almost as throughput as transcriptomics (the catch 22 is: it does require very high level of expertise to perform proteomics at higher standards). - Proteomics is arguably more appropriate than transcriptomics to elucidate functional studies.