Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Introduction to systems biology


Published on

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

Published in: Technology
  • Be the first to comment

Introduction to systems biology

  1. 1.
  2. 2. Thomas Lemberger<br />
  3. 3. What is systems biology?<br />
  4. 4. Fermat’s last theorem*:<br />xn + yn = zn<br />“I have discovered a truly marvelous demonstration of this proposition that this margin is too narrow to contain.”<br />*formulated in 1637, proven in 1995<br />
  5. 5. What is systems biology?<br />“I have discovered a truly marvelous definition of systems biology...*”<br />*formulated in 2010, totally unproven<br />
  6. 6. Example 1: regulatory networks<br />General question: What are the key ‘master regulators’ of a differentiation process or disease state? <br /><ul><li>Specific problem: Transformation of neural cells into aggressive glioma tumor, with mesenchymal phenotype associated with invasiveness, angiogenesis and poor prognosis.
  7. 7. Data: mRNA expression profiles</li></ul>Carro et al. Nature 2009<br />
  8. 8. ARACNe: infers potential TF-target interactions from gene expression profiles (Basso et al. Nat Biotech 2005, Margolin et al. 2006 Nat Protocols).<br />Interactions inferred from pairwise correlations between gene expression across many (>100) samples.<br />Mutual information used as measure of correlation.<br />Indirect links removed as much as possible to keep only potential direct interactions.<br />I(X;Z) <br />Y<br />X<br />Z<br />I(X;Y)<br />I(Y;Z)<br />I(X;Z) ≤ I(X;Y)<br />I(Y;Z) ≤ I(X;Y) <br />Regulatory networks<br />
  9. 9. Neural cell<br />Aggressive glioma<br />Carro et al. Nature 2009<br />Regulatory networks<br />
  10. 10. Regulatory networks<br />‘Master regulator’<br />Mesenchymal Gene Expression Signature<br />Note: master regulators tend NOT to be differentially expressed!<br />
  11. 11. Carro et al. Nature 2009<br />Regulatory networks<br />Neural cell<br />Aggressive glioma<br />
  12. 12. Example 2: dynamics<br />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).<br />Specific problem: The Arabidopsis thaliana circadian clock and the design of its genetic circuit.<br />Data: transgenic reporter<br />
  13. 13. Dynamics<br /><ul><li> Initial model: simple negative feedback loop
  14. 14. Simulation using mathematical model (differential equations) reproduces oscillations of LHY and TOC1 RNA
  15. 15. Problems: simulated TOC1 profile wrong at dusk, no time delay between TOC1 and LHY, insensitive to length of the day </li></ul>Locke et al Mol SystBiol, 2005<br />Locke et al Mol SystBiol, 2006<br />Zeilinger et al Mol SystBiol, 2006<br />
  16. 16. Dynamics<br />Model 3:<br />Fits better to experiments and mutant phenotypes. Prediction on expression profile of Y identifies GIGANTEA as the possible missing link Y<br />Model 4:<br />Incorporates new experimental data. Better predictions. Flexible tracking of dusk and dawn.<br />Model 2:<br />Better. But experiments reveal that cca1;lhy1 mutants retain residual rhythmic activity: is there an additional oscillator?<br />
  17. 17. Dynamics<br />Modeling/Experimentation iterations lead to 3-loop model:<br /><ul><li> Explains (describes?) better the experimental data.
  18. 18. Reveals an interesting design principle: a morning oscillator (Loop III) and an evening oscillator (Loop II) are coupled, which may confer flexibility to the clock to measure the length of the day by tracking dusk and dawn </li></li></ul><li>What is systems biology?<br />Aim: understand a biological process performed by a defined system in terms of all its components.<br /><ul><li>Individual properties vscollective behavior: links levels of organization (one at the molecular level for molecular systems biology)
  19. 19. Systematic aspect => lists of all components (‘omics’), measure all properties and interactions, bioinformatics & computational biology (large scale data integration)
  20. 20. Quantitative aspect => quantitative biology, modeling
  21. 21. A priori, no privileged scale for what represents an interesting biological ‘system’=> small scale (system=pathway), genome-wide (system=cell) to multi-organisms (system=eco-system)</li></li></ul><li>Rapid expansion<br />
  22. 22. Rapid expansion<br />
  23. 23. Rapid expansion<br />
  24. 24. Rapid expansion<br />
  25. 25. Rapid expansion<br />
  26. 26. Rapid expansion<br />+ many other before 2000!<br />
  27. 27. Rapid expansion<br />Quantitative (q) vsomics (Ω) in 2008-9<br />Citation rates<br />Ω<br />q<br />Ω<br />
  28. 28. Scope:<br />integrative genome-scale biology<br />quantitative biology<br />metabolic networks<br />regulatory networks<br />evolution of genomes and biological networks<br />clinical and translational systems biology<br />synthetic biology and genome-scale biological engineering<br />
  29. 29. Comparative and context-dependent omics<br />Mol SystBiol 6:397<br />Mol SystBiol 6:430<br />Mol SystBiol 7:461<br />Mol SystBiol 6:448<br />Mol SystBiol 6:365<br />Mol SystBiol 6:423<br />
  30. 30. Chuang et al. Mol SystBiol 2007<br />Data integration<br />
  31. 31. From networks to dynamics<br />Saez-Rodriguez et al Mol SystBiol2009<br />
  32. 32. Space: re-insert networks into the cell<br />Waks et al, 2011 Mol SystBiol 7:506<br />Di Vetura and Sourjik , 2011 Mol SystBiol 7:457<br />
  33. 33. Cell population dynamics<br />Kirouacet al, 2010, Mol SystBiol6:417<br />Singh et al, 2010, Mol SystBiol6:369<br />
  34. 34. Communities & environment<br />Raes et al, 2010, Mol SystBiol7:473<br />Martin et al, 2007, Mol SystBiol3:312<br />
  35. 35. Synthetic dynamics<br />Balaggadde et al, 2008, Mol SystBiol4:187<br />Chuang et al, 2010, Mol SystBiol6:398<br />Lou et al, Mol SystBiol6:350<br />
  36. 36. Synthetic genomes<br />
  37. 37. Future directions?<br />Data integration: combine several -omics data types<br />Generalization of comparative -omics<br />Re-insert networks into the living cell: time & space<br />Multiplexed genetic engineering<br />Synthetic communities<br />Cell-cell interactions and heterogeneity in cell populations<br />Evolutionary-environmental-ecological sciences<br />Systems medicine:<br />Systems biology of pathogens<br />Drug target prediction and combinatorial therapies<br />Bridging the gap between in vitro and in vivo<br />Reverse translation: from bedside to bench<br />Human systems genetics<br />
  38. 38. Where are you? <br />Systems biology of the neuron.<br />Personal (metabol/endocrin)-omics.<br />Structural interactomics.<br />Experimental evolution of synthetic circuits.<br />
  39. 39.
  40. 40.
  41. 41.
  42. 42. “How do we get from the Jimome & Craigome to systems biology?”George M Church<br />
  43. 43. THE END<br />