Network biology Wang Jie Shanghai Institutes of Biological Sciences

Network biology

Wang Jie

Shanghai Institutes of Biological Sciences

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

Contents

• 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

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

• 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 = 7L = 8k(a) = 6k in(d) = 2l (ad)=1

c e

g

• 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) = 2/7Ca = 2/15feedback loop: a-d-e feed-forward loop: a-c-d

d

a

fb

c e

g

• Most biological networks are scale-free

• Hierarchical network is more modularity, robustness, adaptation.

Content 2) Network models

Hub Module

• Coherent feed-forward loop (cFFL): a ‘sign-sensitive delay’ element (‘AND’ gate) and persistence detector (‘OR’ gate).

Content 3) Network motifs

cFFLfilter out brief spurious

pulses of signal

E. coli arabinose system

a delay when stimulation stops

E. coli flagella system

• 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

Z1 Z2 Z3

X1 X2 X3

Z1 Z2 Z3

Content 4) Biological networks

Nodes: biological molecules (DNA, RNA, protein, metabolite, small molecular), cells, tissues, organisms, ecosystemsEdges: expression correlation,biological (physical, genetic) interaction

Transcription regulation network, Protein-DNA interaction network

Signaling network

PPI

PDI

RPI, RRI

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

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 GlycerolGlucose G3P Pyruvate

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

Content 6) Cont’: Network analysis

F1 F2 F3

A -1 1 0

A p 1 -1 0

ADP 1 0 0

ATP -1 0 0

B 0 -1 1

B p 0 1 -1

C 0 0 1

C p 0 0 -1

• 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

Content 7) Database and Software

• 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

• 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

References

Thank you!