Network biology
Wang Jie
Shanghai Institutes of Biological Sciences
Contents
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Introduction
Conception on network
Network models
Network motifs
Biological networks
Network reconstruction and visualization
Network analysis
Relative database and software
Conclusion
Introduction: Network
• Network is a set of interlinked nodes.
• Biological network is any network that
applies to biological systems, e.g. proteinprotein 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.
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
Content 1) Conception on
network
g
• Nodes (vertices, N): connection points,
e.g. biological molecular.
b
f
a
• Edges (Links, L): connect pairs of
c
e
vertices, e.g. biological interaction.
d
• 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, N = 7
strength of edges (mass and signal flow). L = 8
k(a) = 6
• Shortest path (l, mean path length): path k (d) = 2
in
with the smallest number of links between l (ad)=1
the selected nodes.
Content 1) Cont’: Conception
• Degree distribution, P(k): probability that
g
a selected node has exactly k links. For
b
f
scale-free network, degree distribution
a
approximates a power law P(k) ~ k –γ (γ<3). c
e
Hubs, highly connected nodes.
d
• Clustering coefficient, C(k): C = 2n /
[k(k–1)], measure the degree of
interconnectivity (n) in the neighborhood of P(2) = 2/7
Ca = 2/15
–1
a node. In hierarchical network, C ~ k .
feedback
Modularity, local clustering.
loop: a-d-e
• Network motif: overrepresented circuits, feed-forward
e.g. feedback and feed-forward loops.
loop: a-c-d
Content 2) Network models
• Most
biological
networks are
scale-free
• Hierarchical
network is
more
modularity,
robustness,
adaptation.
Hub
Module
Content 3) Network motifs
• Coherent feed-forward loop (cFFL): a
‘sign-sensitive delay’ element (‘AND’ gate)
and persistence detector (‘OR’ gate).
E. coli
E. coli
flagella system
arabinose system
cFFL
a delay when
filter out brief spurious
stimulation stops
pulses of signal
Content 3) Cont’: Network motifs
• Negative auto-regulation (NAR)
Speed up the response time (SOS DNA-repair
system), reduce cell–cell variation
• Positive auto-regulation (PAR)
X
X
X
• Single-input modules (SIM)
Allow coordinated expression of a group of genes
Z1 Z2 Z3
with shared function
X1 X2 X3 • Dense overlapping regulons (DOR)
As a gate-array, carrying out a computation by
which multiple inputs are translated into multiple
Z1 Z2 Z3
outputs
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
Content 4) Cont’: Biological
networks
Yeast highosmolarity
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
Glucose
Gpd2
Glycerol
Glucose
Pfk26
G3P
Glycerol-3-phosphate
Tdh1/2/3
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
Ap
1
-1
0
ADP
1
0
0
ATP
-1
0
0
B
0
-1
1
Bp
0
1
-1
C
0
0
1
Cp
0
0
-1
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
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).
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
Thank you!