A short overview of the field of systems biology I gave recently at the EMBO YIP sectorial meeting.

2. Thomas Lemberger

3. What is systems biology?

4. Fermat’s last theorem*: xn + yn = zn “I have discovered a truly marvelous demonstration of this proposition that this margin is too narrow to contain.” *formulated in 1637, proven in 1995

5. What is systems biology? “I have discovered a truly marvelous definition of systems biology...*” *formulated in 2010, totally unproven

7. Data: mRNA expression profilesCarro et al. Nature 2009

8. ARACNe: infers potential TF-target interactions from gene expression profiles (Basso et al. Nat Biotech 2005, Margolin et al. 2006 Nat Protocols). Interactions inferred from pairwise correlations between gene expression across many (>100) samples. Mutual information used as measure of correlation. Indirect links removed as much as possible to keep only potential direct interactions. I(X;Z) Y X Z I(X;Y) I(Y;Z) I(X;Z) ≤ I(X;Y) I(Y;Z) ≤ I(X;Y) Regulatory networks

9. Neural cell Aggressive glioma Carro et al. Nature 2009 Regulatory networks

10. Regulatory networks ‘Master regulator’ Mesenchymal Gene Expression Signature Note: master regulators tend NOT to be differentially expressed!

11. Carro et al. Nature 2009 Regulatory networks Neural cell Aggressive glioma

12. Example 2: dynamics General question: Circadian clocks generate biological rhythms of approx 24 hr; oscillations are synchronized to day/night cycle; oscillations are maintained even under constant darkness (or light). Specific problem: The Arabidopsis thaliana circadian clock and the design of its genetic circuit. Data: transgenic reporter

14. Simulation using mathematical model (differential equations) reproduces oscillations of LHY and TOC1 RNA

15. Problems: simulated TOC1 profile wrong at dusk, no time delay between TOC1 and LHY, insensitive to length of the day Locke et al Mol SystBiol, 2005 Locke et al Mol SystBiol, 2006 Zeilinger et al Mol SystBiol, 2006

16. Dynamics Model 3: Fits better to experiments and mutant phenotypes. Prediction on expression profile of Y identifies GIGANTEA as the possible missing link Y Model 4: Incorporates new experimental data. Better predictions. Flexible tracking of dusk and dawn. Model 2: Better. But experiments reveal that cca1;lhy1 mutants retain residual rhythmic activity: is there an additional oscillator?

19. Systematic aspect => lists of all components (‘omics’), measure all properties and interactions, bioinformatics & computational biology (large scale data integration)

20. Quantitative aspect => quantitative biology, modeling

22. Rapid expansion

23. Rapid expansion

24. Rapid expansion

25. Rapid expansion

26. Rapid expansion + many other before 2000!

27. Rapid expansion Quantitative (q) vsomics (Ω) in 2008-9 Citation rates Ω q Ω

28. Scope: integrative genome-scale biology quantitative biology metabolic networks regulatory networks evolution of genomes and biological networks clinical and translational systems biology synthetic biology and genome-scale biological engineering

29. Comparative and context-dependent omics Mol SystBiol 6:397 Mol SystBiol 6:430 Mol SystBiol 7:461 Mol SystBiol 6:448 Mol SystBiol 6:365 Mol SystBiol 6:423

30. Chuang et al. Mol SystBiol 2007 Data integration

31. From networks to dynamics Saez-Rodriguez et al Mol SystBiol2009

32. Space: re-insert networks into the cell Waks et al, 2011 Mol SystBiol 7:506 Di Vetura and Sourjik , 2011 Mol SystBiol 7:457

33. Cell population dynamics Kirouacet al, 2010, Mol SystBiol6:417 Singh et al, 2010, Mol SystBiol6:369

34. Communities & environment Raes et al, 2010, Mol SystBiol7:473 Martin et al, 2007, Mol SystBiol3:312

35. Synthetic dynamics Balaggadde et al, 2008, Mol SystBiol4:187 Chuang et al, 2010, Mol SystBiol6:398 Lou et al, Mol SystBiol6:350

36. Synthetic genomes

37. Future directions? Data integration: combine several -omics data types Generalization of comparative -omics Re-insert networks into the living cell: time & space Multiplexed genetic engineering Synthetic communities Cell-cell interactions and heterogeneity in cell populations Evolutionary-environmental-ecological sciences Systems medicine: Systems biology of pathogens Drug target prediction and combinatorial therapies Bridging the gap between in vitro and in vivo Reverse translation: from bedside to bench Human systems genetics

38. Where are you? Systems biology of the neuron. Personal (metabol/endocrin)-omics. Structural interactomics. Experimental evolution of synthetic circuits.

42. “How do we get from the Jimome & Craigome to systems biology?”George M Church

43. THE END