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Network biology Wang Jie Shanghai Institutes of Biological Sciences.

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Presentation on theme: "Network biology Wang Jie Shanghai Institutes of Biological Sciences."— Presentation transcript:

1 Network biology Wang Jie Shanghai Institutes of Biological Sciences

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

3 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. Introduction: Network

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? M. genitalium 525

5 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 (k in ) and outgoing (k out ) 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. Content 1) Conception on network d a fb N = 7 L = 8 k(a) = 6 k in (d) = 2 l (a  d)=1 ce g

6 Degree distribution, P(k): probability that a selected node has exactly k links. For scale-free network, degree distribution approximates a power law P(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. Content 1) Cont’: Conception P(2) = 2/7 C a = 2/15 feedback loop: a-d-e feed-forward loop: a-c-d d a fb ce g

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

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

9 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 Content 3) Cont’: Network motifs X X X Z1Z1 Z2Z2 Z3Z3 X1X1 X2X2 X3X3 Z1Z1 Z2Z2 Z3Z3

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 Transcription regulation network, Protein-DNA interaction network Signaling network PPI PDI RPI, RRI

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) : Sln1 Hog1 Gpd1/Gpp2 PPI network: Hog1 Pfk26, Hog1 Tdh1/2/3 Metabolism network: Pfk26 + Gpd1 Gpd2 Pfk26 Tdh1/2/3 Glucose Glycerol-3-phosphate Glycerol 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 F1F2F3 A10 A p 10 ADP100 ATP00 B0 1 B p 01 C001 C p 00

15 Database PPI and PDI network: BioGRID, IntAct, STRING, JASPAR, hPDI, cisRED, TargetScan, miRBaseBioGRIDIntActSTRING JASPARhPDIcisREDTargetScanmiRBase Signaling and metabolism network: KEGG, BioCarta, MetaCycKEGG BioCartaMetaCyc Software Network hub motif, and module: Hubba, mfinder, FANMOD, Kavosh, heinz, BioNet, CfinderHubbamfinder FANMODKavoshheinzBioNetCfinder Network reconstruction and visualization: Cytoscape, MATISSE, BioTapestry CytoscapeMATISSEBioTapestry Network analysis: NeAT, CellNetAnalyzer, SBMLNeATCellNetAnalyzerSBML Content 7) Database and Software

16 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). Conclusions

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

18 Thank you!


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