Network biology

1. Network biology Wang Jie Shanghai Institutes of Biological Sciences

2. Contents • Introduction • Conception on network • Network models • Network motifs • Biological networks • Network reconstruction and visualization • Network analysis • Relative database and software • Conclusion

3. Introduction: Network • Network is a set of interlinked nodes. • Biological network is any network that applies to biological systems, e.g. protein-protein interaction networks, transcription regulatory networks, signaling networks. • Network biology quantifiably describes the characteristics of biological networks. • Network modeling qualitatively or quantitatively formulates the rules of networks.

4. What’s biological network for? How do the topology (organization) and dynamics (evolution) of the complex intercellular networks contribute to the structure and function of a living cell? 525 M. genitalium

5. Content 1) Conception on network g • Nodes (vertices, N): connection points, e.g. biological molecular. • Edges (Links, L): connect pairs of vertices, e.g. biological interaction. • Degree (k): the number of connections it has to other nodes. Directed and undirected networks. Incoming (kin) and outgoing (kout) degree. Positive, negative, strength of edges (mass and signal flow). • Shortest path (l, mean path length): path with the smallest number of links between the selected nodes. b f a c e d N = 7 L = 8 k(a) = 6 kin(d) = 2 l (ad)=1

6. Content 1) Cont’: Conception g • Degree distribution,P(k): probability that a selected node has exactly k links. For scale-free network, degree distribution approximates a power lawP(k)~ k –γ(γ<3). Hubs, highly connected nodes. • Clustering coefficient,C(k): C = 2n / [k(k–1)], measure the degree of interconnectivity (n) in the neighborhood of a node. In hierarchical network, C ~ k –1. Modularity, local clustering. • Network motif: overrepresented circuits, e.g. feedback and feed-forward loops. b f a c e d P(2) = 2/7 Ca = 2/15 feedback loop: a-d-e feed-forward loop: a-c-d

7. Content 2) Network models Hub Module • Most biological networks are scale-free • Hierarchical network is more modularity, robustness, adaptation.

8. Content 3) Network motifs • Coherent feed-forward loop (cFFL): a ‘sign-sensitive delay’ element (‘AND’ gate) and persistence detector (‘OR’ gate). E. coli flagella system E. coli arabinose system cFFL a delay when stimulation stops filter out brief spurious pulses of signal

9. Content 3) Cont’: Network motifs X • Negative auto-regulation (NAR) Speed up the response time (SOS DNA-repair system), reduce cell–cell variation • Positive auto-regulation (PAR) • Single-input modules (SIM) Allow coordinated expression of a group of genes with shared function • Dense overlapping regulons(DOR) As a gate-array, carrying out a computation by which multiple inputs are translated into multiple outputs X X Z1 Z2 Z3 X1 X2 X3 Z1 Z2 Z3

10. Content 4) Biological networks Nodes: biological molecules (DNA, RNA, protein, metabolite, small molecular), cells, tissues, organisms, ecosystems Edges: expression correlation, biological (physical, genetic) interaction PPI PDI RPI, RRI Transcription regulation network, Protein-DNA interaction network Signaling network

11. Content 4) Cont’: Biological networks Yeast high-osmolarity glycerol (HOG) response system, consist of signaling, PPI, PDI and metabolism networks Genetic interaction profiles in yeast

12. Content 5) Network reconstruction and visualization • Signaling network • (PDI network): • Sln1Hog1 Gpd1/Gpp2 • PPI network: Hog1 Pfk26, Hog1 Tdh1/2/3 • Metabolism network: Pfk26 + Gpd1 Glucose Glycerol-3-phosphate Glycerol Gpd2 Tdh1/2/3 Pfk26 • Glucose G3P Pyruvate

13. Content 6) Network analysis • Analysis of network feature • Distribution of degree and clustering coefficient, other topology • Identification of key hubs, motifs, modules, pathways (statistical inference) • Network comparison • Between sub-graphs, among random, normal and disease, or tissue/species-specific networks • Network modeling • Boolean, Bayesian, stoichiometric, stochastic and dynamic model

14. Content 6) Cont’: Network analysis

15. Content 7) Database and Software • Database PPI and PDI network: BioGRID, IntAct, STRING, JASPAR, hPDI, cisRED, TargetScan, miRBase Signaling and metabolism network: KEGG, BioCarta, MetaCyc • Software Network hub motif, and module: Hubba, mfinder, FANMOD, Kavosh, heinz, BioNet, Cfinder Network reconstruction and visualization: Cytoscape, MATISSE, BioTapestry Network analysis:NeAT, CellNetAnalyzer, SBML

16. Conclusions • In network, hubs (degree)  important nodes, motifs  mechanism, modules (CC)  function, systems (topology)  behavior • By dynamics analysis, comparison and modeling, the property of sub-graphs and whole network can be partially revealed. • Top to the bottom: from scale-free and hierarchical network to the organism-specific modules, motifs and molecules. (vs. bottom up).

17. References • Alon U. Network motifs: theory and experimental approaches. 2007. Nat Rev Genet • Barabási AL & Oltvai ZN. Network biology: understanding the cell's functional organization. 2004. Nat Rev Genet • Hyduke DR and Palsson BØ. Towards genome-scale signalling-network reconstructions. 2010. Nat Rev Genet • Yamada T and Bork P. Evolution of biomolecular networks — lessons from metabolic and protein interactions. 2009. Nat Rev Mol Cell Biol

18. Thank you!