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Biological Network Analysis
The genomic era
Human genome sequence “completed”, Feb 2001
PubMed abstracts indicate a recent
interest in Systems Biology
Human genome completed
Protein-protein interaction data
• Physical Interactions
– Yeast two hybrid screens
– Affinity purification (mass
spec)
– Peptide arrays
– Protein-DNA by chIP-chip
• Other measures of
‘association’
– Genetic interactions (double
deletion mutants)
– Genomic context
(STRING)
Yeast two-hybrid method
Y2H assays interactions in vivo.
Uses property that transcription
factors generally have separable
transcriptional activation (AD) and
DNA binding (DBD) domains.
A functional transcription factor can
be created if a separately expressed
AD can be made to interact with a
DBD.
A protein ‘bait’ B is fused to a DBD
and screened against a library of
protein “preys”, each fused to a AD.
Systems biology and emerging
properties
Can a biologist fix a radio?
Lazebnik, Cancer Cell, 2002
Building models from parts lists
Protein-DNA
interactions
▲ Chromatin IP
▼ DNA microarray
Gene levels
(up/down)
Protein-protein
interactions
▲ Protein coIP
▼ Mass spectrometry
Protein levels
(present/absent)
Biochemical
reactions
▲none
Metabolic flux ▼
measurements
Biochemical
levels
One framework for Systems Biology
1.
The components. Discover all of the genes in the
genome and the subset of genes, proteins, and other
small molecules constituting the pathway of interest. If
possible, define an initial model of the molecular
interactions governing pathway function (how?).
2.
Pathway perturbation. Perturb each pathway
component through a series of genetic or
environmental manipulations. Detect and quantify the
corresponding global cellular response to each
perturbation.
One framework for Systems Biology
3.
Model Reconciliation. Integrate the observed mRNA
and protein responses with the current, pathwayspecific model and with the global network of proteinprotein, protein-DNA, and other known physical
interactions.
4.
Model verification/expansion. Formulate new
hypotheses to explain observations not predicted by
the model. Design additional perturbation experiments
to test these and iteratively repeat steps (2), (3), and
(4).
From model to experiment and
back again
Continuum of modeling approaches
Top-down
Bottom-up
Data integration and
statistical mining
Need computational
tools able to distill
pathways of interest
from large molecular
interaction databases
(top-down)
Types of information to integrate
• Data that determine the network
(nodes and edges)
– protein-protein
– protein-DNA, etc…
• Data that determine the state of the
system
–
–
–
–
–
mRNA expression data
Protein modifications
Protein levels
Growth phenotype
Dynamics over time
Mapping the phenotypic data to the network
•Systematic phenotyping
of 1615 gene knockout
strains in yeast
•Evaluation of growth of
each strain in the
presence of MMS (and
other DNA damaging
agents)
•Screening against a
network of 12,232 protein
interactions
Begley TJ, Rosenbach AS, Ideker T,
Samson LD. Damage recovery pathways
in Saccharomyces cerevisiae revealed
by genomic phenotyping and interactome
mapping. Mol Cancer Res. 2002
Dec;1(2):103-12.
Mapping the phenotypic data to the network
Begley TJ, Rosenbach AS, Ideker T,
Samson LD. Damage recovery pathways
in Saccharomyces cerevisiae revealed
by genomic phenotyping and interactome
mapping. Mol Cancer Res. 2002
Dec;1(2):103-12.
Mapping the phenotypic data to the network
Begley TJ, Rosenbach AS, Ideker T,
Samson LD. Damage recovery pathways
in Saccharomyces cerevisiae revealed
by genomic phenotyping and interactome
mapping. Mol Cancer Res. 2002
Dec;1(2):103-12.
Network
models can be
predictive
Green nodes represent proteins identified as being required
for MMS resistance; gray nodes were not tested as part of
the 1615 strains used in this study; blue lines represent
protein-protein interactions.
The untested gene deletion strains (ylr423c, hda1, and
hpr5) were subsequently tested for MMS sensitivity; all
were found to be sensitive (bottom).
Begley TJ, Rosenbach AS, Ideker T, Samson LD. Damage
recovery pathways in Saccharomyces cerevisiae revealed
by genomic phenotyping and interactome mapping. Mol
Cancer Res. 2002 Dec;1(2):103-12.
Networks and Network
Topology
Network Example - The Internet
http://www.jeffkennedyassociates.com:16080/connections/concept/image.html
Network Representation
regulates
gene A
gene B
regulatory interactions
(protein-DNA)
gene B
functional complex
B is a substrate of A
(protein-protein)
gene B
metabolic pathways
binds
gene A
gene A
reaction
product
is a
substrate for
Representation of Metabolic
Reactions
Network Measures
• Degree ki
• Degree distribution P(k)
• Mean path length
• Network Diameter
• Clustering Coefficient
Network Analysis
Paths:
metabolic,
signaling pathways
Cliques:
protein complexes
Hubs:
regulatory modules
Subgraphs:
maximally weighted
Graphs
• Graph G=(V,E) is a set of vertices V and edges E
• A subgraph G’ of G is induced by some V’  V and E’  E
• Graph properties:
– Connectivity (node degree, paths)
– Cyclic vs. acyclic
– Directed vs. undirected
Sparse vs Dense
• G(V, E) where |V|=n, |E|=m the number of vertices
and edges
• Graph is sparse if m~n
• Graph is dense if m~n2
• Complete graph when m=n2
Connected Components
• G(V,E)
• |V| = 69
• |E| = 71
Connected Components
•
•
•
•
G(V,E)
|V| = 69
|E| = 71
6 connected
components
Paths
A path is a sequence {x1, x2,…, xn} such that (x1,x2),
(x2,x3), …, (xn-1,xn) are edges of the graph.
A closed path xn=x1 on a graph is called a graph cycle
or circuit.
Shortest-Path between nodes
Shortest-Path between nodes
Longest Shortest-Path
Small-world Network
• Every node can be reached from every other by
a small number of hops or steps
• High clustering coefficient and low meanshortest path length
– Random graphs don’t necessarily have high clustering
coefficients
• Social networks, the Internet, and biological
networks all exhibit small-world network
characteristics
Network Measures: Degree
Degree Distribution
P(k) is probability of each
degree k, i.e fraction of
nodes having that degree.
For random networks, P(k)
is normally distributed.
For real networks the
distribution is often a powerlaw:
P(k) ~ k-g
Such networks are said to
be scale-free
Hierarchical Networks
Detecting Hierarchical Organization
Knock-out Lethality and
Connectivity
1.0E+01
60
-1.91
y = 1.2x
1.0E+00
% Essential Genes
P (k )
50
1.0E-01
1.0E-02
1.0E-03
1.0E-04
40
30
20
10
0
1
10
Degree k
100
0
5
10
15
Degree k
20
25
Target the hubs to have
an efficient safe sex
education campaign
Lewin Bo, et al., Sex i Sverige; Om sexuallivet i Sverige 1996,
Folkhälsoinstitutet, 1998
Scale-Free Networks are Robust
• Complex systems (cell, internet, social
networks), are resilient to component failure
• Network topology plays an important role in this
robustness
– Even if ~80% of nodes fail, the remaining ~20% still maintain
network connectivity
• Attack vulnerability if hubs are selectively
targeted
• In yeast, only ~20% of proteins are lethal when
deleted, and are 5 times more likely to have
degree k>15 than k<5.
Other Interesting Features
• Cellular networks are assortative, hubs tend not to
interact directly with other hubs.
• Hubs tend to be “older” proteins (so far claimed for
protein-protein interaction networks only)
• Hubs also seem to have more evolutionary pressure—
their protein sequences are more conserved than
average between species (shown in yeast vs. worm)
• Experimentally determined protein complexes tend to
contain solely essential or non-essential proteins—
further evidence for modularity.
Identifying protein complexes from PPI data
Identifying protein complexes
from protein-protein interaction
data require computational
tools.
Barabasi & Oltvai, Nature Reviews, 2004
Clustering Coefficient
The density of the network
surrounding node I, characterized as
the number of triangles through I.
Related to network modularity
nI
2n I
CI 

 k  k  k - 1
 
2
k: neighbors of I
The center node has 8 (grey) neighbors
There are 4 edges between the neighbors
C = 2*4 /(8*(8-1)) = 8/56 = 1/7
nI: edges between
node I’s neighbors
Summary: Network Measures
• Degree ki
The number of edges involving node i
• Degree distribution P(k)
The probability (frequency) of nodes of degree k
• Mean path length
The avg. shortest path between all node pairs
• Network Diameter
– i.e. the longest shortest path
• Clustering Coefficient
– A high CC is found for modules