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
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
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
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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?
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19. Systematic aspect => lists of all components (‘omics’), measure all properties and interactions, bioinformatics & computational biology (large scale data integration)
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.
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42. “How do we get from the Jimome & Craigome to systems biology?”George M Church